3 Abstract The rapid success of wireless radio communication has shaped several aspects of our social and work lives. This success is still far from reaching its full potential: with the advent of the Internet of Things, future trends indicate an exponential increase of wireless devices for machine-to-machine communication. Due to the broadcast nature of the shared medium, wireless transmissions are potentially received by all network stations in the communication range of the sender. With an unsynchronized medium access, multiple transmissions may be active at the same time and thus interfere with each other. In consequence, multiple transmissions may collide at the receiver side and can not be properly decoded [1]. For this reason, protocols have been developed on the MAC layer to synchronize the medium access and thus reduce interference eects [2]. One of these approaches in wireless mesh networks (WMNs) is channel assignment. The idea of channel assignment is to minimize the network-wide interference by utilizing non-overlapping channels for otherwise interfering wireless transmissions. This is feasible, since wireless mesh routers are usually equipped with multiple radios and commonly used wireless network technologies, such as IEEE , provide multiple non-overlapping channels [3]. Since IEEE operates in the unlicensed frequency spectrum, the dense distribution of private and commercial network deployments of WLANs in urban areas poses a new challenge. Co-located networks compete for the wireless medium, thus decreasing the achievable network performance in terms of throughput and latency. Therefore, an important issue for ecient channel assignment is to also address external interference. This task is not trivial, since the external networks and devices are not under the control of the network operator and their radio activity is therefore hard to capture. The contributions of this dissertation comprise the design, implementation, and validation of models and algorithms to enable wireless multi-hop networks to become interferenceaware. This includes a measurement-based interference model that determines eciently and accurately the interference relationships among network nodes in large-scale network deployments. A distributed channel assignment algorithm has been developed that considers external sources of interference. The overall solution has been experimentally validated in a large-scale wireless multi-hop multi-radio testbed and has signicantly increased the network performance with regard to the network capacity.

7 I dedicate this dissertation to my wife Bettina, my parents Brunhilde and Klaus, and my grandmother Margarete. I am incredibly grateful for Bettina's unconditional love and support. My parents deserve my sincerest gratefulness for their kind support and encouragement during my studies. Finally, I want to express my gratitude to my grandmother Margarete for her love and patience in my earliest years.

8 This dissertation would not have been possible without the support of so many people in many dierent ways. I owe my deepest appreciation to Prof. Dr. rer. nat. Mesut Güne³ who accompanied me throughout the work of the dissertation and for sharing his great expertise of scientic research. Furthermore, I want to thank him for his constant support and the valuable feedback. I am also deeply grateful to Prof. Dr. rer. nat. Jens-Peter Redlich for supervising my dissertation as part of the METRIK research training group. My deepest gratitude also goes to Dr. Ahmet Sekercioglu for supervising my dissertation and for his kind hospitality on my exciting and unforgettable research stay in his group at the Monash University in Melbourne. I would like to thank Prof. Dr. sc. Joachim Fischer for letting me join the METRIK research training group. The valuable feedback I got on the regular evaluation workshops have accelerated the completion of this dissertation and his generous eorts allowed me to disseminate my research results on several conferences. I would also like to thank my colleagues at Freie Universität Berlin and Humboldt Universität Berlin for the feedback and encouragement from countless conversations. I especially thank my oce mate Bastian Blywis for the fun 4 years. Bastian's expertise and even more his euphoric ways of sharing his knowledge were an inspiration. I am also grateful that I was a able to participate in the initial deployment of the DES-Testbed. The passionate team eort created one of the largest experimental facilities which was very exciting to have at our hands. I would like to thank Oliver Hahm for almost 10 years of mutual studies, work, and friendship. And especially for our daily bike rides through Berlin with contemplating discussions of our research and life. I want to thank all the students of the DES working group, which I had the honor to supervise. Their eorts and even more the discussions of their results were very valuable for the direction of the work in this dissertation. Most notably, I want to thank Matthias Philipp and Simon Seif for their support in the channel assignment experiments. I also want to thank my co-founders of Spreebytes, namely Heiko Will, Eike Send, Stephan Adler, and Kaspar Schleiser. The mutual passion for exciting IT projects served as a great inspiration to me and I am grateful for the growing friendships. Finally, I thank the DAAD for granting me a scholarship for my guest researcher stay at the Monash University in Melbourne, Australia. Berlin, November 2013 Felix Shzu-Juraschek

13 List of Figures Network architecture of wireless mesh networks Channels of IEEE b/g Channel assignment problem Intra-ow, inter-ow, and external interferences Conict graph and multi-radio conict graph Architecture of NET-X DES-Nodes of the DES-Testbed Snapshot of the DES-Testbed topology Scientic workow for a network experiment Architecture of the DES-Testbed Management System (DES-TBMS) Links on the 2.4 GHz frequency band with Ralink USB (27 dbm) Links on the 2.4 GHz frequency band with the Atheros MiniPCI (27 dbm) Links on the 2.4 GHz frequency band with the Atheros MiniPCI (17 dbm) Links on the 5 GHz frequency band Bandwidth per channel on the 2.4 GHz band Bandwidth per channel on the 5 GHz band Experiment setup for measuring the eect of ACI on adjacent ows Results of the LIR of adjacent ows on the 2.4 GHz band Results of the LIR of adjacent ows on the 5 GHz band Results of the LIR of adjacent ows on the 2.4 GHz and 5 GHz bands Experiment setup for measuring the eect of ACI on non-adjacent ows Results of the LIR of non-adjacent ows on the 2.4 GHz band Results of the LIR of non-adjacent ows on the 5 GHz band Results of the LIR of non-adjacent ows on the 2.4 GHz and 5 GHz bands Experiment setup to compare single- and multi-channel networks Results of the multi-hop path interference experiment Example of the link selection for the IAR measurements Example of the link selection for the IRR measurements Eects of inter-path interference in a single channel network Example of the link selection for the STR measurements Example of the link selection and interferers for the EXR measurements. 54 v

14 vi List of Figures Percentage of links tuned to the common global channel per algorithm Throughput results for a single link with iperf and nuttcp Distribution of values of the mean and maximum channel occupancy Standard deviation of the values in CO Mean over 50 experiment replications Correlation of the node degree derived from the communication graph and from the channel occupancy measurements Comparison of the CDF of the node degree based on the communication graph and the channel occupancy measurements Ratio of detected nodes via carrier sensing regarding their distance Aggregated results for the experiments to validate COIM Results for the validation of COIM Architecture of the DES-Chan framework Snapshot of the visualization tool DES-Vis Flowchart of the MICA implementation Fractional Interference Ratio (FNI) for MICA Implicit links with MICA Flowchart of the DGA implementation Snapshot of the network topology after the channel assignment procedure Unidirectional links and the percentage of links on the default channel Channel distribution of links in the network topology Results for the intra-path interference metric (IAR) Results for the inter-path interference metric (IRR) Results for the saturation throughput metric (STR) and the absolute saturation throughput Protocol overhead of MICA and DGA Mean number of interferers according to the utilized interference model Results for STR with 10 channels Results for STR with 6 channels Results of channel congestion measurements at the DES-Testbed Mean channel congestion measured in the DES-Testbed with DES-Sense Comparison of the channel congestion per building of the DES-Testbed Flowchart of the EICA implementation Results of DGA and EICA Mean measured channel congestion over all testbed nodes and channels Mean amount of available channels for EICA Results for the aggregate throughput for 6CBR

15 List of Tables Overview of the dierent IEEE standards Overview of channel assignment algorithms Performance metrics of the channel assignment benchmark Results of experiments with the trac generators iperf and nuttcp Error rates for CO Mean and CO Max for measurements over time Prediction table for the validation of COIM Default conguration parameters for the etxd daemon of DES-Chan Experiment settings for the comparison of channel assignment algorithms Overview of Atheros AR5413 carrier sensing registers Experiment settings for the performance evaluation of EICA vii

16

17 CHAPTER 1 Introduction The rapid success of wireless radio communication has shaped several aspects of our social and work lives. In developed countries cell and smart phones are ubiquitous, allowing people to communicate with each other at almost any time from anywhere [4]. Laptops and tablet computers for work or private use are nowadays equipped with multiple wireless technologies such as WiFi [3] and Bluetooth [5]. The rise of wireless technologies leads to an increase of wireless trac at enormous rates - the global mobile data trac grew about 70% in 2012 alone [6]. Despite the technical challenges of the immense growth, the success of wireless communication is still far from reaching its full potential. With the advent of the Internet of Things, future trends indicate an exponential increase of wireless devices for machine-to-machine communication. It is envisioned that by 2020 between 50 and 100 billion devices will be connected over the Internet [7]. The visionary applications aim to provide new services for both consumers and businesses. Applications range from smart household utilities, such as fridges that keep track of their content and automatically re-order certain goods, to industry applications such as tracking a commercial product throughout its life-cycle [8]. Additionally, small-scale computers are deployed as wireless sensor networks to enable complex applications to improve factory automation and environment monitoring [9]. Regardless of the particular wireless technology and the application scenario, the main characteristic and challenge of wireless communication is the shared medium that is used to transport messages between the communication endpoints [10]. Due to its broadcast nature, wireless transmissions are potentially received by all network stations in the communication range of the sender. With an unsynchronized medium access, multiple transmissions may be active at the same and thus they interfere with each other. Such interference leads to a high risk that multiple transmissions collide on the receiver side and cannot be properly decoded [1]. Therefore, several challenges result from the competition among the network stations for the medium access [2]. This chapter gives on overview of the basic principles of wireless communication and the corresponding technologies that form the foundation for the work in this dissertation. The 1

18 2 Chapter 1. Introduction channel assignment challenge is introduced and the chapter closes with an overview of the contributions of the dissertation. 1.1 Evolution of wireless communication As of today, diverse wireless technologies have been developed and deployed to such a degree that we use many of these often on a daily basis. The dierent technologies can be classied into dierent categories. In wireless single-hop networks, clients connect directly to an access point and communicate to other peers only via that particular access point [10]. The most popular examples of these kinds of networks are cellular networks and infrastructure WLANs [3, 11]. In cellular networks, mobile phones connect to a base station using the licensed frequency spectrum, while in infrastructure WLANs clients connect to an Access Point (AP) using the unlicensed frequency spectrum [10]. In contrast, wireless multi-hop networks, are characterized by the absence of infrastructure equipment such as base stations and access points [12]. In order to compensate for the lack of infrastructure, the clients have to perform tasks typically taken care of by the base station such as relaying packets for other nodes over multiple hops towards the nal destination. Typical examples of multi-hop networks are wireless sensor networks (WSNs) and wireless mesh networks (WMNs) [9,13]. WSNs are usually formed by small-scale lowpower devices to enable monitoring applications. WMNs are less constrained regarding the energy consumption and computational power than WSNs and thus are more suitable for data-intensive applications such as providing Internet access in rural areas. It is not uncommon for both network types to have dedicated nodes that provide gateway functionality to connect the networks to the Internet, for example. The feature of wireless multi-hop networks to be independent of infrastructure equipment makes them suitable in scenarios where a robust telecommunication infrastructure either does not exist or has been disabled, for instance due to a natural catastrophe such as an earthquake [14]. However, the lack of any infrastructure requires multi-hop networks to have a high degree of self-organization in order to achieve a similar reliability as their infrastructure counterpart. Due to relaying the message over multiple hops, the latency and the risk of message loss are higher than in single-hop infrastructure networks. However, an active research community has already solved many of these challenges and is further working on improving the services. The focus of this dissertation is on wireless mesh networks, which are described in more detail next. 1.2 Wireless mesh networks As stated above, the main characteristic of WMNs is the lack of any infrastructure equipment required for the network operation. In consequence, the mesh routers have to provide the basic services of the base stations such as relaying packets for the other nodes [12]. The typical architecture of WMNs is depicted in Figure The network architecture

19 1.2. Wireless mesh networks 3 Wired Backbone Backbone mesh routers Routing mesh clients Non-routing mesh clients Wireless Connection Wired Connection Mesh Routers Figure : Network architecture of wireless mesh networks. The stationary backbone mesh routers communicate over the wireless links with each other and with the mesh clients. Backbone mesh routers may have wired connections to another backbone network and can thus function as gateways to dierent networks, such as the Internet. The mesh clients may be stationary or mobile and may also function as routers by forwarding trac for other mesh clients. comprises three tiers: gateways, backbone mesh routers, and mesh clients. The backbone mesh routers are usually stationary and act as base stations or access points for the mesh clients. They communicate over wireless links with each other and with mesh clients. Some of them may have additional wired connections and can thus function as gateways to different networks, such as the Internet. Mesh clients can be stationary or mobile and may function as routers by forwarding trac for other mesh clients. The architecture and set-up of wireless multi-hop mesh networks have evolved with technological advances. First, wireless mesh networks were designed as single-radio singlechannel networks, in which each network node is equipped with exactly one radio and all nodes operate on the same channel to ensure network connectivity. In single-radio multichannel networks, nodes change the channel of the radio frequently with changing trac patterns, which imposes a certain overhead on the performance for the channel switches. With multi-radio multi-channel networks each node is equipped with multiple radios that operate on dierent channels. This enables the network nodes to communicate simultaneously with multiple neighbors using dierent radios and channels. The multi-radio

20 4 Chapter 1. Introduction Channel width: 22MHz Channel Center Frequency Frequency (GHz) Figure : Channels of IEEE b/g. Due to regulatory constraints, channel 14 is only available in Japan. A possible set of three theoretically non-overlapping channels {1, 6, 11} is emphasized in gray. multi-channel set-up is currently the most common type. The reasons for this development are two-fold. First, o-the-shelf wireless network interface cards (WNICs) have become signicantly cheaper over the last decade [15]. Second, commonly used wireless network technologies, such as IEEE , provide multiple non-overlapping channels [3] Technologies for WMNs Due to the wide availability and low cost of IEEE devices, many WMNs rely on this technology. IEEE b/g supports up to 14 dierent channels on the unlicensed ISM radio band at 2.4 GHz [3]. The distance of the center frequency of two adjacent channels is 5 MHz with a channel width of 22 MHz as depicted in Figure The available data rates range from 1 to 11 Mbit/s for IEEE b and up to 54 Mbit/s for IEEE g. The IEEE a standard, intended for the United States, denes 12 non-overlapping channels on the 5 GHz U-NII band. The IEEE h standard evolved to open the 5 GHz radio band for WLAN devices in Europe. It denes additional restrictions because of the co-existence of military and radar stations that utilize the same frequency band. Available data rates range from 6 to 54 Mbit/s. The IEEE n standard [16] oers data rates of up to 600 Mbit/s using multiple-input and multiple-output (MIMO) technology [17] with orthogonal frequency-division multiplexing (OFDM) [18]. The high data rate is achieved with the spacial diversity due to multiple antennas which allow simultaneous transmission of data streams. IEEE n operates on the 2.4 GHz as well as on the 5 GHz frequency bands. The IEEE n standard allows channel-bonding on the physical layer, which means that two non-interfering 20 MHz wide channels can be used simultaneously. This way, the available data rate can be doubled. This feature, also referred to as 40 MHz mode, decreases the number of available non-interfering channels for IEEE n. An overview of the key features of the dierent standards is given in Table The IEEE s draft [19] addresses WMNs and species standard protocols, such as a routing protocol based on AODV [20], for mesh devices in order to create a WMN. Work on the standard started in 2003 and draft 12.0 has been mostly approved in June A reference implementation for the Linux kernel exists in the mac80211 module that has been

21 1.2. Wireless mesh networks standard Frequency (in GHz) Modulation Channel width (in MHz) Max data rate (in MBit/s) a 5 OFDM b 2.4 DSSS g 2.4 OFDM, DSSS n 2.4 and 5 OFDM up to Table : Overview of the dierent IEEE standards. distributed with the One Laptop Per Child (OLPC) initiative [21]. Of interest for channel assignment procedures is that the standard species a protocol for channel switching. The procedure is primarily dened to satisfy regulatory requirements. A channel switch may be carried out to avoid interference with a radar signal or to ensure the connectivity of the Mesh Basic Service Set (MBSS). The IEEE WiMAX standard also supports multiple non-interfering channels [22]. The standard operates on the unlicensed radio band from 2 to 66 GHz with a exible channel width, thus theoretically supporting many more non-overlapping channels than IEEE a/b/g/n. The future of WiMAX is unclear, however, since big manufacturers have ceased the development of hardware after the roll-out of LTE as another 4G mobile technology for IP-based networks [23] Applications and network deployments Many elds of commercial and non-commercial applications for WMNs evolved in the last decade. A typical application for WMNs is to provide the last mile infrastructure for Internet Service Providers in dense city areas as well as in rural areas and for university and business networks. Several companies oer hard- and software to set up WMNs for business clients and consumers [2427]. Self-organized communities, such as the Freifunk community in Berlin [28], MIT Roofnet [29], and Berlin Roofnet [30], set up WMNs to provide Internet access in dense city areas for the participants. Another use case is emergency scenarios. Disaster management describes a situation after a disaster of natural origin or caused by human beings where the existing communication infrastructure fails, either partially or completely [31]. In this case, a substitution has to be installed in the shortest time possible which should be adequate to support rescue operations. It is obvious that the installed network has to be simple to congure, easy to set up and maintain, and adaptable to a dynamic topology in order to support changes in the number and density of participants [32]. The self-organizing and self-healing characteristics of WMNs make them feasible for these scenarios. Two deployments of such networks in the context of disaster management are the SOSEWIN earthquake early warning system in Istanbul [14] and the Humboldt Wireless Lab-Testbed (HWL) in Berlin [33]. The DES-Testbed at Freie Universität Berlin is a hybrid wireless network consisting of 128 multi-radio mesh routers and a number of wireless sensor nodes in the same order [34].

22 6 Chapter 1. Introduction Research on the DES-Testbed is not restricted to a particular application, as the focus is on development and evaluation of protocols and algorithms on layers 2-4 of the network layer stack. The mesh part of the testbed serves as the experimentation environment for all experiments performed in the scope of this dissertation. A detailed description of the DES-Testbed is given in Chapter The channel assignment challenge Due to the broadcast nature of the shared medium, multiple transmissions in close spatial proximity may interfere with each other. Both the sender and receiver of a wireless transmission may be aected by interference. At the receiver side, simultaneous activity of network interfaces utilizing the same channel in close spatial proximity may result in collisions, thus requiring retransmissions of the message. The sending node may have to defer a message, if the carrier sense threshold is exceeded by ongoing transmissions of near-by nodes in IEEE based networks [3] which leads to an increase of the latency. It has been shown that interference is one of the major causes for performance degradation in wireless networks [35]. For this reason, protocols and mechanism have been developed on the MAC layer to synchronize the medium access and thus reduce interference eects. One of these approaches is channel assignment. The idea of channel assignment is to minimize the network-wide interference by utilizing non-overlapping (also called non-interfering or orthogonal) channels for otherwise interfering wireless transmissions. This is feasible, since commonly used wireless network technologies, such as IEEE , provide multiple non-overlapping channels. The key challenge of the problem is how to assign the available channels in a way that interference is minimized and the network performance in regard of the network capacity is maximized while ensuring network connectivity. A resulting channel assignment using dierent non-overlapping channels can decrease interference but also alter the network topology. When the network interfaces of two neighboring network nodes operate on non-overlapping channels they do not interfere with each other but also the nodes can not communicate directly. Thus, there is a trade-o between channel diversity and network connectivity. A simple scenario with three nodes forming a single-channel and a multi-channel network is depicted in Figure To realize the channel assignment of the multi-channel network permanently, two wireless network interfaces for each node are required to establish the links. This leads to the interface constraint [36], which states that the number of channels assigned to a node can not be higher than its number of radios for such static channel assignments. Traditional channel assignment algorithms aim at minimizing the inter-network interference, by assigning non-overlapping channels to potentially interfering links in the network. However, since the leading technologies for WMNs, such as IEEE , operate in the unlicensed frequency spectrum, the dense distribution of private and commercial

23 1.4. Contributions 7 (a) Single channel network (b) Multi channel network Figure : Channel assignment problem. In (a), channel 1 is assigned to all three links. Thus, simultaneous transmissions may result in interference. By assigning three non-overlapping channels to the links as shown in (b), the links can be activated at the same time without interference. network deployments of WLANs in urban areas pose a new challenge. These co-located networks compete for the wireless medium operating on the same wireless radio channels, thus decreasing the achievable network performance in terms of throughput and latency. Additionally, other non-ieee devices, such as cordless phones, microwave ovens, and Bluetooth devices, operate on the unlicensed 2.4 and 5 GHz frequency bands as well and can further decrease the network performance [37, 38]. With the realization of the Internet of Things (IoT), the number of wireless devices operating in the unlicensed frequency bands is rapidly growing. Therefore, it is an important issue for ecient channel assignment to also address external interference. This task is not trivial, since the external networks and devices are not under the control of the network operator and their radio activity is therefore hard to capture. Since their behavior may vary over time, especially considering mobile devices such as smartphones and laptops, it is also hard to predict and model their future behavior. For this reason, many channel assignment algorithms have neglected the impact of external interference. 1.4 Contributions The contributions of this dissertation comprise the design, implementation, and validation of models and algorithms to enable wireless multi-hop networks to become interferenceaware. The overall solution signicantly increases the network performance by assigning non-overlapping channels for potentially interfering links and considering the activity of external interferers. The approach has been experimentally validated in a large-scale wireless multi-hop multi-radio testbed. The key contributions are summarized in the following list: A measurement-based interference model has been developed, implemented, and validated to identify eciently and accurately interference relationships between network nodes in real network deployments. An external interference-aware channel assignment algorithm that is able to detect

24 8 Chapter 1. Introduction and avoid heavily congested channels has been designed, implemented, and validated in scenarios with high levels of interference. A software-based spectrum analyzing solution has been developed, implemented, and validated that enables the detection of external wireless devices and networks. A framework to support the development process of channel assignment algorithms has been designed and implemented to allow rapid prototyping of approaches in testbed environments. An experimental study of existing channel assignment algorithms in a large-scale wireless testbed delivered insights of the potential performance boost with channel assignment. A software-based spectrum analysis solution has been developed, implemented, and validated that enables the detection of external wireless devices and networks. A methodology for the performance evaluation of channel assignment algorithms with domain-specic performance metrics and scenarios has been designed, implemented, and used for the evaluation of the algorithms developed in this dissertation. 1.5 Structure The remainder of the dissertation is structured as follows. Chapter 2 comprises an introduction to the research eld of channel assignment in WMNs. Chapter 3 describes the DES-Testbed, the wireless experimentation laboratory used in this dissertation. Chapter 4 presents the applied methodology with the focus on the experiment work ow in testbed environments and the evaluation of channel assignment algorithms. In Chapter 5, the channel occupancy interference model (COIM) for the estimation of interference relationships among network nodes is presented. The framework DES-Chan for the development of channel assignment algorithms in testbed environments is presented in Chapter 6. A study on the potential performance gain with channel assignment in a large-scale wireless testbed follows in Chapter 7. An algorithm for external interference-aware channel assignment is presented and evaluated in Chapter 8. A summary and conclusion of the dissertation follows in Chapter 9.

25 CHAPTER 2 Channel assignment in wireless mesh networks This chapter introduces the channel assignment problem and the corresponding concepts and terminology in this research eld. A literature study of existing distributed algorithms for channel assignment is followed by a discussion regarding their feasibility in large-scale wireless multi-radio networks. The chapter ends with a discussion of existing research frameworks for distributed channel assignment in wireless testbeds. 2.1 Network model With the unit disk graph model [39] a network is represented by an undirected Graph G = (V, E), where V is the set of network nodes and E V V is the set of wireless links. The graph is undirected, since bidirectional links are required for communication with technologies that require an ACK mechanism on the link layer such as IEEE [3]. For simplicity, it is considered that all wireless interfaces have the same transmission radius r t and interference radius r i, with r i > r t. It is usually assumed that r i = α r t with 2 < α < 3, see [35]. A link exists between two nodes u, v V if their distance d u,v is smaller than their transmission radius r t, more formally d u,v r t. If such a link exists, it is denoted with l u,v. Two links l u,v and l x,y interfere with each other if at least one of the following distances d u,x, d u,y, d v,x, d v,y is smaller than the interference radius r i [40]. It has to be taken into account, that the unit disk graph model comprises all virtual communication links in the network, regardless of a particular interface conguration. To model the channel utilization, in the network topology model, a link between u, v V exists only if d u,v r t and at least one interface on each node is tuned to the same channel. A link between the two nodes u, v V on channel c in the network model is therefore given with l c u,v. Whereas the unit disk graph model describes all possible links in a WMN, the network topology model only comprises the links that can actually be used for communication. 9

26 10 Chapter 2. Channel assignment in wireless mesh networks c 1 c 1 c 1 c 1 (a) (b) (c) Figure : Intra-ow, inter-ow, and external interferences. Intra-ow interference may occur when two hops on a path utilize the same channel (a). Inter-ow interference results when two hops of two dierent ows interfere with each other (b). External interference is exerted from devices which are not under control by the network operator (c). c Interference Interference is one of the major causes for performance degradation in wireless networks and may aect both the sender and receiver of a wireless transmission [35]. If messages collide at the receiver side due to simultaneous transmissions on the same channel, retransmissions of the messages are required. On the sender side, the sender may have to defer a message, if the carrier sense threshold is exceeded by ongoing transmissions of near-by nodes in IEEE based networks [3]. In WMNs, three dierent sources of interferences can aect the network performance as depicted in Figure Intra-ow interference occurs when successive hops on the path of a single ow utilize overlapping channels. Especially single channel networks are prone to this kind of interference [41]. Inter-ow interference results when two links of dierent ows interfere with each other. As a third source, external interference may occur when devices, which are not under control of the network operator, utilize the same frequency band. Usually, channel assignment algorithms try to reduce inter- and intra-ow interference and leave external interference aside, since it can not be controlled [41, 42]. Still, due to the growing numbers of WMN deployments, especially in urban areas [43], it is likely that external networks co-exist and therefore add to the contention of the wireless medium. These devices are likely to operate in the unlicensed frequency spectrum as well and their radio activity pattern may be very dierent. They can be distinguished into devices that operate on a xed frequency (ZigBee [44]), frequency hopping devices (Bluetooth [45]), and broadband interferers (microwave ovens [37]). However, enabling the network with awareness of such external networks and devices without prior knowledge of their location and radio activity pattern is not a trivial task Interference modeling A crucial task for the development of interference-aware solutions is to accurately determine interference relationships between network nodes. The interference relationship for two network nodes u and v describes if transmissions of v may lead to errors in receiving or sending at node u and vice versa. Estimating accurate interference relationships for a

27 2.2. Interference 11 wireless network deployment is dicult due to the complexity of modeling radio signal propagation especially in indoor environments [46]. For this reason, initially simplied models based on heuristics have been proposed for the interference estimation. One of these is the point interference model, stating that two links interfere if they share a common endpoint [47]. A more sophisticated model denes for each node (or more precisely each radio when nodes are equipped with multiple radios) a transmission radius r t and an interference radius r i, with r t < r i. A transmission is correctly received at node u, when only one node of all the nodes in the interference range of u transmits at a time. Models based on this assumption usually approximate the m-hop neighborhood of a node u as the interference set [48] which consists of all nodes whose transmissions may interfere at u. A typical value is m = 2, meaning the interference range of a node is twice its communication range. This model has been rst described as protocol model [35]. With these simple models, the interference estimation can be calculated without much eort based on the local neighborhood information of a network node. However, several studies have shown, that these simplications do not hold in reality and the predicted interference relationships were not accurate for indoor network environments [46, 49, 50]. For this reason, measurement-based methods have been proposed for interference modeling that promise a higher degree of accuracy [46,51]. In these methods, measurements are carried out in the network to determine the interference relationships between the network nodes. While measurement-based models deliver an higher accuracy in predicting interference relationships, the required eort to perform these measurements is signicantly higher [46]. Measurement-based interference models are discussed in detail in Section Conict graphs Conict graphs 1 describe interference relationships between all links in a network and thus quantify the network wide interference level [52]. The conict graph is constructed based on the communication graph of the network and an interference model. The conict graph G C is given with G C = (V C, E C ), where V C corresponds to the edges E of the network graph G = (V, E). An edge in E C denotes, that the two corresponding edges in V C interfere with each other according to the utilized interference model. The conict graph has been extended to the multi-conict graph (MCG) in [53] in order to model multi-radio nodes. The dierence is, that in the MCG, G M is given with G M = (V M, E M ), where V M is the number of links between wireless interfaces instead of network nodes as in the original conict graph. The edges E M are then created in the same way as in the conict graph, whereas two vertices in V M are connected with an edge, when the two links between the wireless interfaces interfere with each other. Examples for a conict and multi-radio conict graph are depicted in Figure Also referred to as interference graphs or contention graphs.

28 12 Chapter 2. Channel assignment in wireless mesh networks A B C D AB BC CD (a) A1 B1 A1 B2 B1 C1 B2 C1 (c) Figure : Conict graph and multi-radio conict graph. (a) shows the network graph G = (V, E) with V = {A, B, C, D} and E = {(A, B), (B, C), (C, D)}. The resulting conict graph, assuming that only direct neighbors are interferers, is depicted in (b) with the corresponding graph structure G C = (V C, E C ) with V C = {AB, BC, CD} and E C = {(AB, BC), (BC, CD)}. In (c) the multi-radio graph is depicted when node B is equipped with two radios and all remaining nodes with only one radio. C1 D1 (b) 2.3 Problem formulation of channel assignment The idea of channel assignment is to minimize the network-wide interference by utilizing non-overlapping channels for otherwise interfering wireless transmissions. By exploiting the benets of orthogonal channels for otherwise interfering transmissions, channel assignment aims at improving the network performance, especially in regard to the network capacity. The key challenge is how to assign the available channels in a way, that interference is minimized while ensuring the network connectivity. A resulting channel assignment using dierent non-overlapping channels can decrease interference but also alter the network topology. When the network interfaces of two neighboring network nodes operate on nonoverlapping channels, they do not interfere with each other but also the nodes can not communicate directly. Thus, there is a trade-o between the channel diversity and the network connectivity. For this reason, most algorithms introduce mechanisms to preserve the network topology throughout the channel assignment procedure, for instance by tuning one interface per node permanently to a common network-wide global channel [48]. The problem of channel assignment has been reduced to variations of the graph coloring problem and has thus proven to be NP-hard [36,40,54,55]. Thus, most proposed distributed channel assignment algorithms build on greedy methods in order to locally minimize the interference levels. Several challenges for ecient distributed channel assignment algorithms have been identied. Channel oscillation describes the situation in which two links periodically switch channels because the channel switch for the rst link leads to sub-optimal channel assignment on the second link and vice versa [41]. Another undesirable phenomenon is the ripple eect which describes the situation in which one channel switch is followed by several subsequent channel switches throughout the network [56]. This happens when the channel of

29 2.4. Classication of channel assignment algorithms 13 the rst hop of an m-hop path is changed, and in consequence the channel assignment is not optimal anymore on the second hop. Therefore, the channel is switched on the second hop as well, and the situation repeats for all m hops on the path. The described phenomena may prevent the stabilization and convergence of distributed channel assignment algorithms. Therefore, it is important to develop appropriate counter-measures, such as the synchronization of channel switches. Some algorithms also introduce restrictions regarding the channel assignment choices, such that each channel-link combination may only be assigned once throughout the channel assignment procedure [36, 40]. While this helps guaranteeing the convergence and stabilization of the algorithm, it may lead to sub-optimal channel assignment and render the algorithm unadaptive to topology changes. 2.4 Classication of channel assignment algorithms Over the last decade, many channel assignment algorithms have been developed for dierent scenarios [41, 57, 58]. This resulted in a wide range of dierent approaches tailored to specic network technologies, network architectures, and application scenarios. Because of the variety, the algorithms can be classied according to the following key properties. First, channel assignment algorithms can be considered centralized or distributed [41]. Centralized channel assignment approaches rely on a central control instance that calculates the channel assignment for the whole network. This entity is often referred to as channel assignment server (CAS) [53]. The CAS gathers network topology information, calculates the channel assignment based on the global network view, and noties the network nodes about the result to adjust their channel assignment accordingly. In distributed channel assignment approaches, the algorithm runs on every network node and executes channel assignment decisions taking only local information into account. The communication overhead for a CAS does not exist in distributed approaches. Still, communication among the nodes is necessary to exchange local information and to notify neighbors of changes in channel assignment. Distributed algorithms are considered to be less prone to node failures because they do not rely on a CAS which may constitute a single point of failure [41]. Also, they are usually more adaptive to a dynamic network topology in regard to node mobility and node failures because the corresponding topology changes can be handled locally. Still, distributed algorithms lack the advantage of using a global network view for the channel assignment calculations. This may lead to suboptimal results considering the network-wide channel assignment. Distributed approaches must also solve challenges concerning the retrieval of the channel usage in their neighborhood and the propagation of channel switches. Usually neighborhood information is exchanged periodically, which also allows to adapt to network topology changes. In order to ensure that channel switches are noticed and do not result in dead interfaces or channel oscillation, channel switches are usually propagated in the neighborhood with simple handshake protocol mechanisms [41]. Another classication considers the frequency of channel switches on a network node. In

30 14 Chapter 2. Channel assignment in wireless mesh networks literature, channel assignment approaches are classied as dynamic or static. In dynamic approaches, channel switches may occur frequently, in the extreme for every subsequent packet a dierent channel is chosen [59]. The limiting factor for dynamic algorithms is the relative long channel switching time with commodity IEEE hardware, which is in the order of milli seconds [60]. Static approaches in contrast, switch the interfaces to a particular channel for a longer period, usually in the order of minutes or hours [48]. Hybrid approaches combine both methods. 2.5 Related work: Distributed channel assignment algorithms Many algorithms for channel assignment in multi-channel multi-radio networks have been proposed in the last years. Therefore, only a selection of approaches is discussed in the following. The focus of this related work study is on distributed and external interferenceaware algorithms, which are closest to the research contribution of this dissertation. For a more complete list, we refer to the surveys of algorithms in [41,57,61]. The algorithms described in this section make several common assumptions, for example that a number of non-overlapping channels C are available and that the number of interfaces K on every node is smaller than C (C K). Usually, the algorithms consider the mesh network as a stationary backbone, meaning that the mesh routers are immobile, although some algorithms consider node failures and low mobility, which makes them adaptive to topology changes. Also, all surveyed algorithms consider only bi-directional links because of the ACK-mechanism of IEEE unicast transmissions. After a description of the dierent approaches, we introduce a classication for a conclusive discussion Distributed Greedy Algorithm - DGA The distributed greedy algorithm (DGA) assigns channels to network nodes, or more precisely, to network interfaces [48,62]. Intuitively, each node tries to minimize the interference by assigning the least used channels in its interference set. The interference set of a node n consists of all nodes and their channel assignment whose transmissions aect sending and receiving at node n. To preserve the network connectivity, one interface on every node is operating on a global common channel and is not used for channel assignment. For any additional interfaces (referred to as switchable interfaces), the channel is determined based on an interference cost function f I as given in the following equation. f I (f 1, f 2, α) = max(0, α f 1 f 2 ) (2.1) The spectral overlap of two channels is used to determine the level of interference in the interference set. The cost function takes the center frequencies f 1 and f 2 of two radio channels and the additional parameter α into account. α denotes the minimum frequency dierence of orthogonal channels. A value of f I = 0 means that the channels are orthogonal and thus do not interfere with each other. In each iteration, the algorithm considers exactly

31 2.5. Related work: Distributed channel assignment algorithms 15 one switchable interface and calculates the interference cost for all available channels K with the channel assignment of the interference set. The result is the channel c best with the lowest overall interference cost. If c best is dierent than the currently assigned channel for the interface in question, a channel switching procedure based on a three-way handshake including all nodes of the interference set is initiated. As an additional constraint, only channels can be assigned that are used by at least one neighboring node, in order to avoid isolated interfaces. The advantage of the algorithm is its simple greedy nature and the proven convergence [62]. The interference cost function is simple to calculate but only relies on the spectral distance of the available channels. Spatial distance between nodes, obstacles, and external interferences which have an impact on the interference and link quality in real networks are not considered Minimum interference channel assignment - MICA The minimum interference channel assignment (MICA) assigns channels to links instead of interfaces and is therefore topology preserving [36]. In other words, all links in the single channel network, also exist in the multi channel network after the channel assignment procedure. This way, the approach is independent of the overlaying routing algorithm. The authors formulate the channel assignment problem as minimizing the total network interference by minimizing the number of edges in a conict graph [52]. The problem is reduced to the Max K-cut problem, thus proving that it is NP-hard. The proposed distributed algorithm is based on a greedy approximation algorithm for the Max K-cut problem given in [63]. At the network initialization, all links are assigned to the same channel. After retrieving the local communication graph of the m-hop topology (with m 1) and constructing the conict graph V c, each nodes iterates over all link-channel combinations (u, c), identifying the combination which results in the largest decrease of interference in the local neighborhood. The largest decrease is achieved with the combination (u, c) that removes most edges in the local conict graph V c compared to all other combinations. Thereby, the interface constraint has to be respected, which means that no more channels can be assigned to a node than it has interfaces. Before the network node n selects the link-channel combination (u, c), the following criteria have to be met. Node n is incident to the link u Node n owns link u, meaning that its ID is higher than the ID of the other node incident to u Applying channel c does not violate the interface constraint on both nodes The combination (u, c) has not been tried before at node n

32 16 Chapter 2. Channel assignment in wireless mesh networks The combination (u, c) results in the highest decrease of interference compared to all other combinations The algorithm terminates, if no further (u, c) combination can be found that matches all criteria. Once a combination (u, c) is found, node n initiates a channel switching procedure with the corresponding neighbor of the link in question. As the main advantage due to the link-based channel assignment, the algorithm does not alter the network topology. Thus, routing algorithms can operate independently on top of the channel assignment. The main weakness of the algorithm is the restriction that each link and channel combination can only be changed once, thus the algorithm is not adaptive to topology changes Sridhar - SRI09 Sridhar et al present an extension of the previously discussed MICA algorithm that additionally takes the expected trac-load on the nodes into account [40]. The algorithm diers in that a link is now owned by the node whose expected trac load is highest, compared to the ID-based approached in the original MICA. Due to the strong similarity, the same advantages and weaknesses account for this algorithm as to the previous one. The main dierence is the extension with a trac load matrix of expected trac for every link, which enables this approach to perform load-aware channel assignment Breadth-rst search channel assignment - BFS-CA The rst algorithm for channel assignment that considers external interference has been proposed in [53]. The centralized algorithm incorporates the external interference by periodically assessing the interference levels of the available channels. This is done by setting a wireless interface into monitor mode, in which frames of co-located IEEE stations can be received that pass the frame check sequence (FCS). This way, co-located IEEE interfaces in the communication range can be detected. The congestion of the channel is measured by analyzing the captured packets for the particular channel. The results are passed to a CAS which then calculates the network wide channel assignment and sends the decisions back to the mesh nodes. Once the topology and channel condition information is collected at the CAS, the channel assignment is calculated based on a breadth-rst search (BFS) algorithm originating at the gateway. Thus, links closer to the gateway are prioritized. After sorting the links according to the distance to the gateway, the highest ranked channels are assigned to them taking the channel conditions into account. The algorithm has been the rst one to introduce a measurement-based approach capturing the channel conditions in the channel assignment procedure. However, with this method only IEEE radios are captured. Also, remote interferers, which reside outside the communication range but inside the interference range, are not captured because

33 2.5. Related work: Distributed channel assignment algorithms 17 their transmissions would not pass the FCS. The same method to detect external interference with an interface in monitor mode is also used in [64]. A hybrid channel assignment algorithm is then extended that considers the channel conditions Net-X A MAC-layer protocol as a joint solution of channel assignment and routing is proposed in [65, 66]. The set of network interfaces on each node are divided into xed interfaces, which stay on a xed channel, and switchable interfaces, which can be switched dynamically to particular channels. If a node wants to communicate with a neighbor, it tunes one of the switchable interfaces to the channel of a xed interface of the receiving node. Thus, the algorithm qualies as an hybrid approach. The crucial part of this approach is the way how channels are assigned to the xed interfaces. The authors present two dierent algorithms for this task. For a simple solution, a function may be used which calculates the channel for a xed interface based on the node ID. As an alternative, neighborhood information is considered for the channel selection. For this, HELLO-packets with the assignment of xed radios are exchanged periodically, which allows each node to learn about the channel assignment in the 2-hop neighborhood. Each node selects the least used channel for its xed radio. While the former approach is only feasible in a static and known network topology, the latter introduces more overhead but can adapt to topology changes. Additionally, the challenge of broadcasting in a multi-channel network is addressed. The presented solution is to send a broadcast packet once for each channel. The algorithm uses one packet queue for each channel. By considering the queue size of each channel and dening minimum and maximum periods for a switchable interface to stay on one channel, the number of channel switches can be reduced. The NET-X framework implements this approach in a testbed environment which is presented in Section The advantages of this approach are that no default channel has to be used to ensure the network connectivity and that fairness is addressed with the queue management. A weakness constitutes the high implementation eort to ensure an ecient dynamic channel assignment. Changes in the Linux kernel have been necessary to reduce the channel switching time and implement channel-based packet queues Urban-X Urban-X provides a cross layer architecture for multi-radio wireless mesh networks tailored to urban scenarios with co-located wireless devices operating in the unlicensed frequency bands [67,68]. It comprises a channel assignment algorithm, a spectrum sensing component to assess external interference, and an extended AODV multi-path routing protocol. The spectrum sensing component is based on an energy detection scheme to measure the channel busy time. Thus, the channel load can be estimated. The channel assignment algorithm is an extension of the algorithm initially proposed with NET-X [65]. On

34 18 Chapter 2. Channel assignment in wireless mesh networks each network node, one interface is set to a xed channel for receiving and further switchable interfaces can be switched dynamically to particular channels. If a node wants to communicate with a neighbor, it tunes one of the switchable interfaces to a channel of a xed interface of the receiving node. The channel assignment algorithm for the xed interfaces incorporates the estimated load per channel and the current internal network channel assignment. The algorithm has the advantage to consider external interference and the spectrum sensing component is promising for the task to estimate the channel load. However, Urban-X has been realized so far as a simulation framework in which modeling the signal propagation in complex environments is limited Skeleton assisted partition free channel assignment - SAFE The skeleton assisted partition free channel assignment (SAFE) algorithm preserves a minimal spanning tree (MST) of the network topology [69]. Thus, the network connectivity is ensured, but not all links of the single channel network may exist after the channel assignment procedure. The algorithm comprises two components. A random channel assignment is applied if K < 2 C, where C is the number of wireless network cards on every node and K the number of non-overlapping channels. Due to the pigeonhole principle, two nodes will share a common channel although they are assigned randomly, thus preserving the network connectivity. The second component of the algorithm introduces the condition that all edges of a MST, the skeleton, of the network have to be preserved when K 2 C. For this, every node randomly chooses a channel set with C 1 channels, leaving one interface unassigned. The node broadcasts its chosen channel set, and if links to all skeleton neighbors are already established it assigns a random channel to the unassigned interface. Otherwise it tries to establish links with the not connected skeleton neighbors by assigning a channel which is in the channel set of all skeleton neighbors. If there is such a channel, it is assigned to the interface, if not, a global common channel is used for these links. The advantage of this algorithm is that it ensures the connectivity of the network using a MST of the virtual network links. Still, the algorithm is prone to node failures since this loss of a skeleton edge may partition the network Neighborhood nodes collaboration to support QoS - NNCQ The neighborhood nodes collaboration to support QoS (NNCQ) algorithm is a joint approach for routing and channel assignment [70]. At the network initialization, all nodes possess a connectivity matrix containing all available network links. During the network operation, the packet loss rate of a particular link is used as channel switching metric, which is calculated periodically by monitoring sent and received packets. Based on this metric, the algorithm consists of two phases: 1. Monitoring phase: The nodes monitor the packet loss rate for all adjacent links.

35 2.6. Discussion Channel switching phase: If the packet loss rate for a link reaches a certain threshold, the channel switching phase is executed. In this phase, the sending node searches a dierent node-disjoint path using the connectivity matrix. If a new path is found, the sender initiates a channel switch with the next hop on the path. The least used channel for the link is chosen. After the successful channel switch, multiple routes for the same destination are available. Therefore, a route selection process has to be executed. A source routing is used with NNCQ and either the best route is chosen for all transmission or the routes are used round-robin like. The latter method may result in frequent channel switches. The advantage of this approach is the consideration of the packet loss rate which also incorporates external interference and allows nodes to quickly react to link quality changes. As a weakness, each node relies on a global connectivity matrix that is never updated and thus renders the approach unable to cope with network topology changes. 2.6 Discussion Classication Keys The following classication keys are used to characterize the presented channel assignment algorithms. Distributed algorithm (DA): Denes if the algorithm is of distributed nature. Channel Switching Frequency (CSF): Denes the frequency of channel switching. In dynamic or fast channel switching approaches, channel switches may occur frequently, up to for every subsequent packet. In static or slow channel switching approaches interfaces are switched to a particular channel for a longer period. Hybrid approaches combine both methods. External Interference (EI): Denes if external interference is considered by the algorithm. This may be addressed directly by detecting sources of external interference or indirectly by measuring the packet loss rate of a particular link. Link Connectivity Preserved (LCP): Denes if all virtual links of the network are preserved after channel assignment. Conict Graph Minimization (CGM): Denes if the problem is formulated such that the number of edges in the conict graph shall be minimized. Interference Model (IM): Denes which interference model is used for the interference estimation. Failure / Mobility (FM): Denes the degree of adaptivity of the algorithm. Node failures and node mobility lead to network topology changes, which result in nodes joining and leaving the neighborhood of other nodes.

36 20 Chapter 2. Channel assignment in wireless mesh networks Fairness (FA): Denes if the approach considers fairness in regard to network resources. Testbed Evaluation (TE): Denes if the approach was implemented and evaluated in a testbed environment. Trac Load (TL): Denes if the expected trac load is considered in the algorithm. Channel Oscillation (CO): Denes if the channel oscillation problem is addressed by the algorithm Routing Metric (RM): Denes which routing metric is used for the evaluation to exploit channel diverse paths. The characteristics of each algorithm in regard to the classication keys are summarized in Table The following discussion compares the dierent channel assignment algorithms and investigates trends for future algorithms Summary All of the presented algorithms are of distributed nature apart from BFS-CA [53]. In this centralized algorithm, a dedicated channel assignment server (CAS) calculates the network-wide channel assignment. The algorithm is listed, since it was the rst one to consider external interference by using spectrum sensing to assess the channel conditions. Of the surveyed approaches, all but NNCQ [70], NET-X [66], and URBAN-X [67] are static, meaning that channels are assigned to interfaces for a longer period of time. One reason for this is the relative long channel switching time, which is in the order of milliseconds for current IEEE hardware [60]. Also the implementation of dynamic channel assignment is more complex. For instance, NET-X [66] and URBAN-X [67] require changes to the Linux kernel and the drivers of the wireless interface in order to reduce the channel switching time [65]. New hardware is likely to reduce the channel switching time, thus making dynamic schemes more attractive for future approaches. Most of the discussed algorithms try to reduce inter- and intra-ow interference and leave external interference aside, since it can not be controlled. However, with the focus shifting to urban scenarios with a dense distribution of co-located wireless devices and networks, external interference has gained more attention recently. Of the discussed algorithms, BFS-CA [53], Urban-X [67], and NNCQ [70] consider external interference in the channel assignment. While BFS-CA [53] and Urban-X [67] use spectrum sensing to assess the channel conditions, NNCQ [70] indirectly adapts to external interference by periodically monitoring the packet loss rate for a given link.

38 22 Chapter 2. Channel assignment in wireless mesh networks All surveyed approaches preserve the network connectivity in order to avoid network partitions caused by channel assignment. All approaches apart from SAFE [69] also preserve the link-based connectivity. This means that all virtual network links between nodes are preserved. Operating one interface per node on a common global channel to preserve the link-based connectivity is done in DGA [48] and BFS-CA [53]. Most of the surveyed approaches use simple distance-based interference models and dene the interference set as the m-hop neighborhood, with 2 m 3. BFS-CA [53] states that the used 2-hop model is exchangeable with a more accurate, measurementbased model but the eort for measurements is not in the scope of the algorithm study. With MICA [36], SRI09 [40], BFS-CA [53], several approaches formulate the channel assignment problem as minimizing the number of the edges in the conict graph or multi conict graph. Of these algorithms, only MICA [36] has been evaluated with the fractional network interference (FNI) metric as described in Section 4.2. Adaptivity to topology changes caused by node failures and mobility is not of a high concern of the presented approaches. MICA [36] and SRI09 [40] are not adaptive because they prevent the re-assignment of a channel-interface combination in order to ensure the convergence of the algorithm. Adaptivity is addressed by running the algorithm periodically in DGA [48] and SAFE [69]. One reason why adaptivity is not considered an important feature is the assumed network architecture which is a stationary mesh backbone. Still, adaptivity becomes more important with the trend to more volatile, large-scale deployments with an increasing number of mobile nodes and possible node failures. The algorithms BFS-CA [53], Net-X [66], Urban-X [67], and SAFE [69] consider fairness among the nodes for the channel assignment decisions. The fair distribution of network resources is of higher concern in gateway-oriented network architectures such as [56, 71], in which trac patterns are more predictable and are usually limited to ows from mesh router to gateway and vice versa. In the presented approaches the expected trac load is only considered in SRI09 [40] and NNCQ [70]. The reason for this is that most approaches are targeted at peer-to-peer scenarios. An implementation candidate for a testbed environment has been evaluated for DGA [48], MICA [36], BFS-CA [53], and Net-X [66]. In the eld of channel assignment, a testbed evaluation is of high value due to the complex modeling of signal propagation and the resulting interference eects. Most approaches prevent channel oscillation by using a three-way handshake to announce channel changes as in DGA [48], MICA [36], SRI09 [40], and NNCQ [70]. Channel oscillation can not occur with centralized algorithms, such as BFS-CA [53]. Several algorithms have been evaluated with interference-aware routing metrics. DGA [48] and BFS-CA [53] use WCETT [72], while Net-X uses MCR [66]. In the scope of Urban- X [67], a custom routing metric has been designed, incorporating the packet transmission delay, packet error rate, and the channel switching time. Based on the discussion of the algorithms and classication keys, we try to predict the trends for future research in channel assignment. With the exploding number of wireless

39 2.7. Related work: Frameworks for distributed channel assignment 23 devices, channel assignment algorithm will have to consider external interference to avoid an signicant impact on the network performance [37, 38]. Especially since many applications of WMNs, such as providing Internet access to mobile clients, are envisioned mainly for urban scenarios. With the trend towards large-scale network deployments in regard to the number of nodes, the need for the algorithms to be adaptive towards node mobility, node failure, and changes in the channel conditions becomes more important. Finally, due to the huge eort to obtain measurement-based interference estimations, all algorithms rely on simple distance-based interference models. The recent development of more ecient measurement-based approaches [50, 73] combined with the results of studies showing the inaccuracy of the simple models [46, 49], will allow channel assignment algorithms to prot of the more accurate interference estimations. 2.7 Related work: Frameworks for distributed channel assignment The implementation process of channel assignment algorithms in testbed environments yields many challenges and pitfalls, since common operating systems are not designed to support channel assignment out of the box. Thus, the programmer has to deal with operating system specics, drivers for the wireless interfaces and the capabilities and limitations of the particular hardware. This is also reected in the results of the survey of the previous section, in which only about half of the algorithms have been evaluated in a testbed environment. A development framework for channel assignment algorithms can simplify the implementation eort by introducing an abstraction layer for common operating system specic tasks, such as channel switches and the conguration of the wireless interfaces. Furthermore, the analysis of several diverse distributed algorithms has shown that despite their dierent strategies for channel assignment, a couple of common key services are required for almost all distributed algorithms. This includes a neighbor discovery service, node to node message exchange for channel switch synchronization, and interference models for the network-wide interference estimation and for creating conict graphs. By providing a service library of such commonly used services, a framework for channel assignment can signicantly speed up the development process. This is especially helpful, if several strategies are implemented to compare their performance. As next, we present frameworks that have been created to support researchers in the development process of channel assignment algorithms FreeMac With the release of the MadWiFi driver and the OpenHAL [74], an open source implementation of the binary hardware abstraction layer (HAL) distributed by Atheros, several frameworks emerged to support research on MAC protocols for commodity IEEE hardware. FreeMAC [75], based on its predecessor MadMAC [76], is such a framework

40 24 Chapter 2. Channel assignment in wireless mesh networks that provides an API to simplify multi-channel MAC protocol development. The channel switching time is a crucial issue for the performance of dynamic channel assignment algorithms, and therefore, FreeMAC has altered the channel switching procedure to make it faster. Any neighbor discovery operations that usually take place after a channel switch have been removed. The achieved channel switching time, including a hardware reset of the wireless network interface and operating system specic operations, was measured on a Linux-based test system with about 4 ms [75]. FreeMAC can function as the foundation of dynamic channel assignment algorithms that require fast channel switching. However, the framework does not provide any other services for channel assignment algorithms, such as neighbor discovery or communication among network nodes NET-X The NET-X framework was created to implement the fast channel switching algorithm described in [66] for a wireless testbed environment based on the 2.4 Linux kernel [65, 77]. The algorithm divides the available wireless network interfaces on each node into xed interfaces, which stay on a single channel for receiving, and switchable interfaces for sending, which can be dynamically switched to dierent channels depending on the receiver. The research focus of the NET-X framework is on algorithms for the channel assignment of the xed interfaces. The implementation of the NET-X algorithm requires several changes to the Linux network stack and to the driver of the wireless network interfaces which is supported by the framework. Among them are per-channel queues, in order to store messages temporarily that are to be transmitted on a dierent channel than the switchable interface is currently operating on and the reduction of the channel switching time. The architecture of NET-X comprises the channel abstraction layer (CAL), the kernel multi-channel routing (KMCR), and the userspace daemon as shown in Figure The CAL comprises broadcast and unicast components, queuing and scheduling mechanisms for the channel queues, and implements the conguration of the wireless network interfaces through the userspace daemon. The KMCR provides the necessary functionality to support reactive routing protocols, such as packet buering during the route discovery procedure. The userspace daemon comprises a multi-channel routing protocol and the interface handling. An abstraction layer for interface handling is provided with a set of functions to control the interface capabilities. These comprise the radio parameters that aect the network performance, such as the utilized channel and transmission power. Experiments with the NET-X framework have been performed to evaluate dierent strategies for the channel assignment of the xed interfaces [78] and for QoS-provisioning. Extensions have been developed for queue management [79]. In summary, the services provided by NET-X are directly derived from the requirements of the implemented algorithm [66]. The framework is not intended to function as a foundation to develop dierent

41 2.7. Related work: Frameworks for distributed channel assignment 25 Multichannel Routing Userspace Daemon Interface Management Linux TCP/IP Stack Kernel Multi-Channel Routing Support Route Handling Broadcast Unicast Channel Abstraction Layer Queuing and Scheduling Wireless Interface Con guration wlan0 wlan1 Figure : Architecture of NET-X. Displayed are the three components of NET-X the channel abstraction layer (CAL), the kernel multi-channel routing (KMCR), and the userspace daemon and their interconnection. distributed channel assignment algorithms, the focus is on algorithms to assign channels to the xed interfaces. Data structures for network and conict graphs are not provided, also a topology monitoring functionality has not been in the scope of the framework.

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43 CHAPTER 3 DES-Testbed: Wireless experimentation laboratory The Distributed Embedded Systems Testbed (DES-Testbed) is a hybrid wireless network located on the campus of Freie Universität Berlin. It is hybrid in a way, that all DES- Nodes comprise a wireless mesh router equipped with multiple IEEE a/b/g radios and an MSB-A2 sensor node [80]. The deployment of the testbed started in 2008 and since then, the testbed has been used for experimentation in several European (OPNEX [81], WISEBED [82]) and national funded projects (G-Mesh-Lab [83]). All experiments described in this dissertation have been performed on the DES-Testbed. The chapter starts with a description of the network architecture and the hard- and software components of the DES-Testbed. The experimentation methodology presents the workow for testbed experiments and the corresponding tools to assist researchers in the studies. The chapter closes with experimental results concerning the network topology and the characteristics of the IEEE radio channels. 3.1 Architecture The DES-Testbed comprises about 128 indoor and outdoor DES-Nodes. The hybrid DES- Nodes consist of a mesh router and a sensor node (see Figure 3.1.1) in the same enclosure, thus forming an overlapping WMN and WSN. The DES-Nodes are deployed in an irregular topology across several buildings on the computer science campus as depicted in Figure Besides the DES-Testbed, several in-parallel IEEE networks exist in order to provide Internet access to students and sta members on the campus. These networks are not under our control and thus contribute to external interference. We treat this as a condition that is also likely to be expected in a real world scenario. A study on the levels of external interference generated by these co-located networks is presented in Section Each DES-Node is equipped with three IEEE radios. One of the interfaces is a 27

44 28 Chapter 3. DES-Testbed: Wireless experimentation laboratory (a) Indoor DES-Node (b) Outdoor DES-Node (c) MSB-A2 sensor node Figure : DES-Nodes of the DES-Testbed. The indoor DES-Node (version 2) with a custom enclosure is shown in (a), the outdoor DES-Node in (b). An MSB-A2 sensor node shown in (c) is attached via USB to each DES-Node and resides in the same enclosure. Ralink RT2501 USB stick and the other two are Mini PCI cards with an Atheros AR5413 chipset. The cards use the rt73usb [84] and ath5k [85] drivers, which are part of the Linux kernel. While the Ralink WNICs are IEEE b/g devices operating in the 2.4 GHz band, the Atheros WNICs additionally support the IEEE a standard on 5 GHz. The MSB-A2 sensor nodes of the DES-Testbed are equipped with a CC1100 transceiver operating in the ISM bands at 863 to 870 MHz. The radio channels utilized by the MSB-A2 are therefore orthogonal to the frequency band used by the IEEE WNICs. In theory, not all 14 channels in the 2.4 GHz and 24 channels in the 5 GHz band can be legally used for the ad-hoc mode in Germany due to regulatory requirements. The regulatory domains are dened by the European Telecommunications Standards Institute (ETSI) [86] for Europe, the Federal Communications Commission (FCC) [87] for the USA, and the Telecom Engineering Center (TELEC) [88] for Japan. However, since the DES- Testbed is an experimental environment, we removed all regulatory constraints. A static regulatory domain database for the Linux kernel allows us to remove all restrictions. Additionally, we discovered a hard-coded limitation for the ad-hoc mode in the upper 5 GHz band for the ath5k driver for the Linux kernel version which had to be removed as well. As a result, all available channels on both frequency bands can be used for experimentation purposes on the DES-Testbed. Further details of the architecture of the DES-Testbed are available in the technical reports [8991].

45 3.2. Methodology 29 Figure : Snapshot of the DES-Testbed topology. The DES-Nodes are distributed over several buildings on the computer science campus. 3.2 Methodology An important design goal of the DES-Testbed is to support the experimenter throughout all steps of the scientic workow. The rst crucial challenge is to provide convenient and reliable means to access the testbed and the network nodes for experimentation. The most open way is to enable remote experimentation, which means that scientists can access the testbed and run experiments remotely over the Internet. Additionally, an experimental environment may provide appropriate tools that guide the researcher throughout the experimental process and help to avoid pitfalls. Before describing the available tools for the DES-Testbed, we rst dene the task of experimentation and the experiment workow for network experiments in general Experimentation Independent of particular experiment environments and research subjects, requirements exist which have to be fullled by all researchers and their experiments to ensure meaningful results. We consider the following requirements as the most important ones each experiment should meet: Unbiased: An experiment should be as general as possible and should not be modied to t a particular experiment environment or setting. Rigorous: An experiment should focus only on the crucial matter it wants to investigate to reduce side eects. Repeatable: An experiment should be executed as often as desired to obtain statistically sound results. Reproducible: Results from dierent executions of an experiment in the same environment should deviate only slightly. Whereas some of these requirements depend fully on the scientist, such as the invention and design of experiments [92, 93], experiment environments are capable of supporting

46 30 Chapter 3. DES-Testbed: Wireless experimentation laboratory Adaptation Conception Formalization Schedule... Query Discovery Data Replication Monitoring Cleanup Figure : Scientic workow for a network experiment. The life cycle of an experiment consists of three phases: design, execution, and analysis. others. This can be achieved by providing tools that assist the researcher throughout the experiment workow as described next Experiment workow In general, every experiment consists of the following three phases: Design Execution Analysis Experimentation is usually an iterative process resulting in a feedback loop between the analysis and design phases as depicted in Figure Results of a rst version of an experiment might reveal critical parameters in the design of the experiment. Based on these results, the experiment can be ne-tuned. Experiment design Every experiment starts with an idea. The idea is then developed into an ne-grained experiment description with clear instructions for every single step. For this process, a domain expert with profound knowledge of the research subject is required. The parameters for the particular experiment have to be specied. For networking experiments, these

47 3.2. Methodology 31 Figure : Architecture of the DES-Testbed Management System (DES-TBMS). parameters usually include the number of network nodes, their location, trac patterns, and many more. The resulting parameter space may quickly become very large. Methods for the Design of Experiment (DoE) should be used to minimize the parameter space and thus reduce experiment run time [93]. Experiment execution Once an experiment has been designed and formally described, it is ready to be scheduled for execution. The experimentation environment has to be in a dened state before and after the execution of the experiment. For this reason, a conguration phase precedes the experiment execution and a cleanup phase follows after the execution. Usually, an experiment is replicated several times in order to obtain statistically sound results. For this reason, a tool that allows the automated execution of experiments proves helpful for the researcher. Domain-specic languages (DSL) have been used to develop a formalized and machine-readable experiment description languages for automated experiment executions [94, 95]. Throughout the experiment, the state of the testbed nodes is monitored in order to detect unexpected behavior such as crashes and reboots. Experiment analysis Finally, the raw data produced by the experiment is processed to extract the prior dened performance metrics. The performance metrics are then investigated to validate or invalidate the hypothesis of the experiment. Based on the results, the original experiment may be ne-tuned or adapted and eventually rescheduled for execution Testbed Management System (TBMS) The DES-Testbed Management System (DES-TBMS) [96] comprises tools to support the design, automated execution, and analysis of experiments on the DES-Testbed. The software framework consists of six components, each dedicated to a specic task in the experiment workow. The architecture and the relationships between the components are depicted in Figure 3.2.2, a description of each component follows. DES-Cript is a domain specic language (DSL) based on XML, which denes and describes network experiments in a holistic way [95, 97]. As the structure of the underlying network is abstracted, DES-Cript is not limited to the DES-Testbed. Each DES-Cript le

48 32 Chapter 3. DES-Testbed: Wireless experimentation laboratory contains a general information section, followed by the available network nodes assigned into groups with particular roles. Next, actions are assigned to a group or individual nodes. As existing DES-Cript experiment descriptions can be edited and reused, they can be used to isolate critical parameters. Moreover, DES-Cript les provide a well-dened experiment documentation without any further eort. DES-Exp provides an experiment manager which is responsible for the scheduling and execution of experiments dened in the DES-Cript experiment description. DES-Exp also assures a dened state at the beginning of each experiment and its replications. DES-Web provides a web interface to DES-Exp, which allows to create, modify, and schedule experiments using DES-Cript. The network monitoring tool DES-Mon is based on SNMP and retrieves the network state from the DES-Nodes. DES-Mon collects data from the wireless interfaces, the kernel routing table and data from the sensor nodes. DES-Vis is a 3D-visualization software based on the JavaView framework [98] to display gathered data obtained from experiments or to show the current state of the network. For routing algorithms it can display the existing links between the network nodes or color the links and nodes for the evaluation of channel assignment algorithms. The evaluation tool DES-Eval enables the post-processing of the experiment results supporting workows for an automatic evaluation process. It provides several congurable input, processing, and output modules. In addition, new modules can easily be written and integrated. Existing modules support input from log les or database records, statistical analysis using R [99], and output as plotted graphs. 3.3 Characteristics of the network topology and channel conditions This sections covers several experiments that have been carried out on the DES-Testbed in order to validate common assumptions of WMNs. The experimentally determined channel characteristics are an important input for channel assignment algorithms. For instance, adjacent channel interference (ACI) can be measured and thus the existence of possible non-interfering channels can be validated Network topology and link quality As a rst experiment, we determine the network topology of the DES-Testbed with the number of existing links and their corresponding quality. As link metric, we use the expected transmission count (ETX) [100]. The measurements were performed with the ETX daemon of the DES-Chan framework as described in Section 6.2. We also investigate the impact of the particular channel on the network topology. For this, we determine the number of links and their respective ETX values separately on each channel of the 2.4 GHz and 5 GHz frequency band. It is expected that the signal range decreases with a higher frequency [36]. Therefore, the quality of links on channels at 2.4 GHz is likely to decrease when the channel is switched to 5 GHz. The experimental validation is important since a link that exists

49 3.3. Characteristics of the network topology and channel conditions 33 Figure : Number of links and their quality on the 2.4 GHz frequency band using the Ralink USB WNIC with a transmission power of 27 dbm. About 1100 links have been detected per channel, the majority of them are of high quality with ETX < 2. when using a channel on the 2.4 GHz band may not exist when the corresponding WNICs are tuned to a channel of the 5 GHz spectrum. If a channel assignment algorithm does not consider this constraint, the network connectivity may be aected after the channel assignment procedure. We measured the number and quality of links using all available 128 DES-Nodes. For the experiments, we tuned one WNIC on each mesh router to the same channel. We started the ETX daemon to determine the amount of links and the corresponding link quality for each network node. The ETX values were measured periodically for 3 minutes on each channel with a sliding window of 10 seconds and an beacon interval of 1 second. We executed the measurements for both WNIC types of the DES-Nodes, the IEEE b/g Ralink USB adapter which is attached to a 2.4 GHz antenna and the IEEE a/b/g Atheros-based MiniPCI card which is attached to a dual-band antenna. Figure shows the results using the Ralink WNIC on 2.4 GHz with a transmission power of 27 dbm as reported by the driver. We can observe that on all channels we have about 1100 unidirectional links. The majority of the links are of high quality, expressed by ETX < 2. The number of medium quality links for which 2 ETX < 5 is very low. Low quality links expressed by ETX > 5 have been observed more often. Figure shows the results of the same experiment using the Atheros WNIC, also with the transmission power set to 27 dbm. On each channel, we observe roughly the same number of links, however, the absolute number of links compared to the previous experiment is much higher, in average it is about The results are similar in that the majority of links is of high quality. Again, medium quality links for which 2 ETX < 5 are rare. The absolute number of links is about 60 % higher compared to the results with the Ralink network adapter. The results of a measurement using the Atheros card with the transmission power set to 17 dbm is shown in Figure With the lower signal power,

50 34 Chapter 3. DES-Testbed: Wireless experimentation laboratory Figure : Links and their quality on the 2.4 GHz frequency band using the Atheros MiniPCI WNIC with a transmission power of 27 dbm. The number of links is signicantly higher compared to the previous experiment using the Ralink adapter. Figure : Links and their quality on the 2.4 GHz frequency band using the Atheros MiniPCI WNIC with a transmission power of 17 dbm. With the reduced transmission power, the results are similar to the ones in the rst experiment using the Ralink adapter. we get about the same number of links as in the rst experiment with the Ralink adapter. Figure shows the results using the Atheros WNIC on 5 GHz with a transmission power of 27 dbm. Again, the majority of the links are of high quality, whereas the number of medium quality links is very small. The amount of links and their quality does not vary a lot among the particular channels of the 5 GHz spectrum, except for channel 44. The carrier sensing statistics revealed a high channel congestion on this particular channel caused by external devices that leads to the lower number of links. The expected decrease of the number of links for the higher frequencies can be observed, in average we count about 750 links per channel.

51 3.3. Characteristics of the network topology and channel conditions 35 Figure : Number of links and their quality on the 5 GHz frequency band. We observe signicantly fewer links compared to the experiments on the 2.4 GHz band. In order to investigate the eects of the channel usage to the network topology, we take a closer look at the high quality links with ETX < 2 throughout all four experiments. We observed, that the number of links in the whole network depends on the utilized WNIC, the frequency band, and on the transmission power. The highest number of links have been observed using the Atheros-based WNIC with a transmission power of 27 dbm on the 2.4 GHz band. Setting the transmission power to 17 dbm results in a similar number of links compared to the experiment with the Ralink adapter. The fewest number of links have been reported with the Atheros interface on the 5 GHz band, which is as expected. The experiments validate the assumption that the network topology is dependent on the particular frequency band in use and also on the particular wireless network adapter. Therefore, it can not be assumed that the link quality or even the network connectivity stays the same if channels are switched without considering the transmission power and the frequency band Channel conditions As next, we investigated the conditions regarding the throughput of all available channels in the 2.4 and 5 GHz band. We selected 40 high quality links with ETX = 1 for each channel and measured the throughput on these links sequentially. This way, no parallel measurements were carried out in order to avoid interference eects. For the throughput measurements, we used iperf and generated UDP trac for 30 seconds with a data rate of 6 MBit/s which corresponds to the data rate settings of the WNICs. The results for the throughput measurements in the 2.4 GHz band are shown in Figure The throughput of channel 14 is close to the theoretic maximum with low deviation. Channel 14 is exclusively used in the DES-Testbed due to regulatory constraints which we do not follow in our experimental testbed (see Section 3.1). The lower results for the remaining channels can be credited to the co-located WLAN networks on our campus,

52 36 Chapter 3. DES-Testbed: Wireless experimentation laboratory Figure : Througput per channel on the 2.4 GHz band. Figure : Throughput per channel on the 5 GHz band. which operate on the channels 1, 6, and 11. Also, the eects of adjacent channel interference are visible for the neighboring channels. As a result, all channels but channel 14 suer from external interference in the 2.4 GHz band. The results for the channel conditions in the 5 GHz band are shown in Figure Compared to the results of the 2.4 GHz channels, the achievable throughput of all channels is close to the expected maximum. This can be credited to the fact, that the co-located WLAN networks do not operate on those channels. As a result, the levels of external interference is much lower on the channels in the 5 GHz band than in the 2.4 GHz band and thus should be preferred for experiments Adjacent Channel Interference (ACI) IEEE b/g oers three non-overlapping channels, for instance {1, 6, 11}, and all available channels in IEEE a are non-overlapping. This means in theory, that concurrent transmissions on these channels should not interfere with each other. In practice, experiments on dierent experimental platforms have shown, that the non-interfering characteristics do not hold for many reasons [36,54,72,101103]. The causes for this eect are board crosstalk, radiation leakage, and a small distance between antennas of simultane-

53 3.3. Characteristics of the network topology and channel conditions 37 Mesh router Wireless interface UDP ow Figure : Experiment setup for measuring the eect of adjacent channel interference on adjacent trac ows. ously active radios. Among them, antenna distance has the most severe eect on the performance [104]. To avoid the near-antenna eect, the experimentally specied minimum distance between two antennas is about 1 m. Since mesh routers are usually more compact, it is almost impossible to design a multi-radio mesh router with sucient antenna distance. This is also the case with DES-Nodes, on which the three WLAN antennas are mounted with a distance of about 30 cm. Therefore, we also expect side-eects on the theoretical non-interfering channels, which are subject to experimentation in this section. One of the proposed measurement-based interference estimation schemes is the link interference ratio (LIR) [46]. For two links l u,v and l x,y the LIR is dened as LIR lu,v,lx,y = T lu,v,lx,y +T lu,v,lx,y lu,v lx,y T lu,v +T lx,y where T lu,v is the unicast throughput for link l u,v when only this link is active and T lu,v,lx,y l u,v is the unicast throughput for the link when the link l x,y is active simultaneously. The LIR expresses the impact of interference two links exert on each other by relating the aggregate throughput of both links when they are active individually to the aggregate throughput when they are active simultaneously. A LIR value of 1 indicates that the two links do not interfere at all, whereas a LIR value of 0.5 means that the aggregate throughput is halved when both links are active at the same time. The LIR is suitable to investigate the impact of the channel distance on two simultaneous transmissions. Therefore, in a rst experiment we measure the LIR of two links being adjacent to the same node for a varying channel distance to investigate the eect of intrapath interference. In a second experiment, two links with dierent sender and receiver pairs which are in each others interference range were chosen. This experiment will give insights on the impact of inter-path interference. For both experiments we use two Atheros MiniPCI cards with the ath5k driver. The auto data-rate algorithm is used and RTS/CTS disabled. The channels of the links are sequentially set to all possible combinations on each frequency band. We perform the experiment for all channel combinations of the 2.4 GHz and frequency band, the 5 GHz frequency band, and nally using both bands simultaneously. The experiment has been repeated 40 times for each channel distance. Adjacent trac ows Based on the network topology of the DES-Testbed, we select a subset of 5 high quality links with an ETX value of 1. For each node pair, we selected one node as sender and the other as receiver, as depicted in Figure In order to measure the LIR we generate two

54 38 Chapter 3. DES-Testbed: Wireless experimentation laboratory Figure : Results of the LIR of adjacent ows for channel combinations on the 2.4 GHz band. The LIR of two links in respect to their spectral distance is shown. The median for all channel combinations is about 0.6. UDP unicast ows from one of the routers (sender) to the other (receiver). Each ow is generated with iperf with a data rate of 54 MBit/s for 30 seconds. After the sequential ows, we start both ows another time simultaneously. We measure the individual and aggregate throughput and compute the LIR. We chose this scenario because it is common in multi-hop WMN where a multi-radio node utilize more than one radio at the time in order to increase the overall throughput. Results for the 2.4 GHz band The results for the channel combinations on the 2.4 GHz band are depicted in Figure Unfortunately, they show that in the DES-Testbed no pair of channels of the 2.4 GHz band are non-interfering for adjacent trac ows. The median of the LIR values is about 0.6 regardless of the used channel combination, which means that the aggregate throughput is almost halved when two interfaces are activated simultaneously. Concluding from the results, a channel assignment with the highest possible spectral distance would only lead to a minor increase of the throughput. As already mentioned, we credit these results to the near-antenna eect of the DES-Nodes. Results for the 5 GHz band The results for a subset of all possible spectral channel distances of the 5 GHz band are depicted in Figure It can be observed that the median of the LIR value increases with channel distances of up to 180 MHz. This rise of the LIR is much slower than expected, but the results show that the median is about 0.8 for a channel distance of at least 80 MHz. For a channel distance of 320 MHz and more the LIR decreases again, which we did not

55 3.3. Characteristics of the network topology and channel conditions 39 Figure : Results of the LIR of adjacent ows for channel combinations on the 5 GHz band. The median of the LIR value increases with channel distances of up to 180 MHz and then decreases again. expect. In a rst investigation, this seems to be related to the link quality. We used tcpdump to monitor the received signal strength indication (RSSI) values for each correctly received frame. We observed that the lower the RSSI values are, the lower the LIR values are for a increasing channel distance. Results for both frequency bands In the last set of experiments for adjacent ows, we selected only channel combinations from both available 2.4 GHz and 5 GHz frequency bands. One sender/receiver pair of WNICs is tuned to channel c 1 {1, 13} whereas the other is tuned to channel c 2 {36, 64, 100, 140, 149, 165}. The results, as depicted in Figure , show that the median of the LIR is between 0.8 and 1 and therefore only a small decrease of performance can be observed. Discussion of the results Unfortunately, the results of the experiments dier vastly from the theoretical assumptions. For all channel combinations using only the 2.4 GHz band, a LIR of about 0.6 was measured, which is only a minor improvement to the single channel network scenario. Minor interference eects are observed with a channel distance of at least 80 MHz on the 5 GHz band. Therefore, two simultaneously active ows should make use of both frequency bands, where a LIR of about 0.8 was measured.

56 40 Chapter 3. DES-Testbed: Wireless experimentation laboratory Figure : Results of the LIR of adjacent ows for channel combinations on the 2.4 GHz and 5 GHz bands. The median of the LIR for all channel combinations is about m Mesh router Wireless interface UDP ow 5 m Figure : Experiment setup for measuring the eect of adjacent channel interference on the LIR with non-adjacent trac ows. Two node pairs are selected which are located in the same room. Non-adjacent trac ows In this study, we measure the LIR for two non-adjacent ows. For this, two pairs of DES- Nodes located in a single room are used. The experiment setup is depicted in Figure The experiment steps and parameters are the same as in the previous experiment. Results for the 2.4 GHz band The results for the channel combinations on the 2.4 GHz band are depicted in Figure With a channel distance of 30 MHz and more, the median of the LIR is usually above 0.8 which implies that a signicant higher throughput can be achieved with a channel distance of at least 30 MHz.

57 3.3. Characteristics of the network topology and channel conditions 41 Figure : Results of the LIR of non-adjacent ows for channel combinations on the 2.4 GHz band. With a channel distance of 30 MHz, the median of the LIR is usually above 0.8 Figure : Results of the LIR of non-adjacent ows for channel combinations on the 5 GHz band. From a channel distance of 60 MHz, the median of the LIR is close to 1. Results for the 5 GHz band The results for the 5 GHz band are depicted in Figure From a channel distance of 40 MHz, the median of the LIR is close to 1, which means that there are hardly any interference eects.

58 42 Chapter 3. DES-Testbed: Wireless experimentation laboratory Figure : Results of the LIR of non-adjacent ows for channel combinations using the 2.4 GHz and 5 GHz bands. The median of the LIR is close to 1. Results for both frequency bands For the last experiment, we selected only channel combinations from both available 2.4 GHz and 5 GHz frequency bands. The results, as depicted in Figure , show that the median of the LIR is close to 1 for all channel combinations and therefore no interference eects are observed. Discussion of the results Although none of the channel combinations in the 2.4 GHz band allow completely noninterfering transmissions, a minimum channel distance of 30 MHz should be used for simultaneously active ows in order to achieve the highest possible throughput. This results in three possible channels {1, 7, 13} for an ecient channel assignment for non-adjacent ows. On the 5 GHz band a minimum spectral channel distance of 40 MHz is sucient to experience only negligible interference eects. Using both bands simultaneously, hardly any interference eects could be measured with the median of the LIR being around 1. The results for non-adjacent ows show fewer impact of interference as the corresponding experiments with adjacent ows. We assume the main causes for these results being the greater antenna distance for the experiments with non-adjacent ows (5 m to 0.3 m) Multi-hop path interference In this experiment, we validate the gained knowledge about the channel characteristics on the DES-Testbed with a manual channel assignment. For this, we create a chain topology of 5 mesh routers, on which we start trac ows over up to four hops. First, we apply the same channel to all links in the chain topology, thus creating a single channel network

59 3.3. Characteristics of the network topology and channel conditions 43 chan 13 chan 13 chan 13 chan 13 chan 13 (a) chan 64 Mesh router Wireless interface UDP ow chan 36 chan 100 (b) Figure : Experiment setup to measure throughput in a single- and multi-channel network. A subset of the mesh routers is selected to create a chain topology. In (a), we apply the same channel to all links, thus creating a single channel network scenario. In (b), we manually assign channels which promise an increase of throughput based on the results of the previous experiments. scenario as depicted in Figure (a). We then start an UDP ow with iperf from the rst node of the chain to the second node. Afterwards we start an UDP ow from the rst node to the third and so on, up to the last node in the chain. We congured the WNICs with the xed data rate of 6 Mbit/s and send the UDP ow for 30 s with the same data rate. We repeat the experiment for each hop 40 times. The results for the single channel network scenario for the throughput in relation to the hop count are depicted in Figure a. As expected for the single channel network case, the throughput is more then halved on the rst hop and keeps dropping with an increasing hop-count. Based on the results of the channel characteristics experiments, we then manually assign channels in a way, that promises the biggest decrease of interference eects. As observed from the experiments on adjacent ows, both frequency bands should be used for the respective WNICs. We expect the throughput to be signicantly higher compared to the single channel network. We apply the channels {13, 36, 64, 100} to the links as depicted in Figure (b) with which we expect to exhibit only minor interference. The results for the multi-channel network scenario are depicted in Figure b. As expected, the manual channel assignment leads to a higher throughput if the hop-count is bigger than 1. It only drops slightly with the increasing hop-count which implicates that the interference eects have been reduced signicantly with the chosen channel assignment. These results show that the experimentally determined channel characteristics also hold in multi-hop scenarios and underline the potential performance gain that can be achieved by an ecient channel assignment.

60 44 Chapter 3. DES-Testbed: Wireless experimentation laboratory (a) Results for the single-channel path experiment. (b) Results for the multi-channel path experiment. Figure : Results of the multi-hop path interference experiment. For the single channel network case, the throughput is more than halved on the rst hop and keeps on dropping with an increasing hop-count as depicted in (a). With the manual channel assignment, the throughput can be signicantly increased as shown in (b). 3.4 Discussion The experiments were performed to gain insights on the network topology and the channel characteristics in the DES-Testbed. We rst showed, that the network topology and the quality of network links depend on the utilized WNIC and the frequency band. In a second series of experiments, the eects of ACI have been investigated. The LIR of two links is signicantly lower for adjacent than for non-adjacent trac ows. However, using channels on both frequency bands also promises a higher throughput for adjacent trac ows. The results of the experiments have been validated with a manual channel assignment in a chain topology spanning 4 hops. The throughput has been signicantly increased compared to the single channel network scenario. A comparison of the experiment results to the common assumptions of channel assignment algorithms yields some interesting deviations. First, the assumption of orthogonal channels as theoretically oered by IEEE and considered in many channel assignment algorithms does not hold in practice. In contrast, if the experimental results indicate that the actual number of available channels is signicantly reduced which may aect the performance of the algorithms. Second, channel assignment algorithms usually assume that a link between two nodes or radios exist or not throughout the network operation. However, the experiments have shown, that the link quality may depend also on the WNIC characteristic and on the used frequency band. Therefore, when designing channel assignment algorithms, it should be considered that channel switches may alter the link quality or even the network connectivity.

61 CHAPTER 4 Methodology This chapter covers the methodology of the experimental studies performed for this dissertation. The applicability of common performance evaluation methods in computer science to the domain of channel assignment is discussed and examples are given how the performance of channel assignment algorithms has been measured in previous works. Subsequently, the channel assignment benchmark (CAB) is presented, comprising domainspecic performance metrics and scenarios for the performance evaluation of channel assignment algorithms in wireless testbeds. Excerpts of this chapter have been published in [105]. 4.1 Motivation The methodology of this dissertation is located in the domain of empirical and experimental research in large-scale wireless networks. Developing implementation candidates of channel assignment algorithms and evaluating their performance in real network environments, has thus gained high signicance. Several design goals have to be taken into account for an holistic experiment methodology, the most important ones are the following: Performance comparison of algorithms: The methodology should allow to compare the performance of a wide range of algorithms, thus giving insights on the feasibility of dierent strategies. Portability across dierent experimentation environments: The methodology should be generic to a degree that it can be easily ported. This way, the performance of channel assignment algorithms can be compared across multiple testbeds with dierent types of network nodes and network topologies. Minimize experiment run time: Since experiments in wireless testbeds are executed in real-time and are labor-intensive, a solid and sound experimentation methodology is required to minimize the testbed usage while still retrieving meaningful results. Ecient performance metrics and scenarios are required to accomplish this task. 45

62 46 Chapter 4. Methodology After briey discussing common tools for the performance analysis, we introduce the channel assignment benchmark (CAB). CAB has been designed to meet the introduced design goals thus allowing a comparison of dierent channel assignment approaches across dierent experimentation facilities. 4.2 Performance evaluation methods Benchmarking Benchmarking is a well known method in computer science for the performance evaluation of a system under test (SUT) [106,107]. For the evaluation, usually a program or a suite of programs is run on the SUT under reference conditions. Performance metrics are dened together with scenarios that allow to measure these metrics. The performance score of a SUT can be compared intuitively with the numbers scored by other systems, which allows a comparison of the performance of dierent solutions Performance evaluation of channel assignment algorithms Many dierent methods have been used for the performance evaluation of channel assignment algorithms in previous works. This comprises using dierent measurement procedures and performance metrics, dierent experimentation environments, and dierent tools and programs (i.e., network trac generators). For these reasons, the comparison of the performance based only on the published results alone is limited. In general, the goal of the performance evaluation in this domain is to measure the reduction of interference eects due to the channel assignment. The developed performance metrics that have been used in previous works can be distinguished into two classes. The rst class comprises performance metrics that measure the decrease of interference achieved by the channel assignment algorithms. One example for such a metric is the fractional network interference (FNI) metric, which has been dened in [36] and used for further approaches in [108]. The FNI is the ratio of the number of edges of the conict graph [1,52] of the single channel network and the number of edges in the conict graph after the channel assignment. The FNI is feasible for algorithms that model the interference with a conict graph and gives an insight of the performance in terms of interference reduction according to the used interference model. However, the results are only comparable to algorithms that also model the interference with a conict graph. The second class of performance metrics avoids this limitation by measuring the gain in network performance after the channel assignment. One common metric of this class is the network saturation (or network capacity) which describes the maximum load the network can carry and which is also used in other domains for performance evaluation. For channel assignment algorithms, the network saturation can be measured and compared for dierent algorithms or the single channel network as a baseline.

63 4.3. Channel Assignment Benchmark (CAB) 47 Another challenge for the comparability of the performance is that the algorithms are evaluated in dierent experimentation environments, comprising analytical analysis, simulation studies, and experiments in real network deployments such as wireless testbeds. Recently, experimentation in wireless testbeds has gained in importance, since often-used simplied interference models, such as the m-hop interference model [35], have been proven to be not very accurate for real network deployments [46, 49, 109]. However, performance measurements on dierent testbeds can usually not be compared directly. Characteristics of the particular testbeds have to be taken into account, such as the network topology and the network environment. Additionally, the testbeds may dier in number of nodes, number of network interfaces per node, and the number of available orthogonal channels. 4.3 Channel Assignment Benchmark (CAB) The channel assignment benchmark (CAB) has been developed for the performance evaluation of channel assignment algorithms in wireless testbeds. CAB comprises performance metrics in respect to the domain-specic goal to reduce interference and scenarios to measure the performance of channel assignment algorithms based on these metrics. In the channel assignment domain, the SUT comprises the particular channel assignment algorithm and the wireless testbed on which the performance evaluation is carried out on. The developed performance metrics and the benchmark are not limited to a particular testbed and can be applied easily to other existing systems Performance metrics and scenarios Considering channel assignment algorithms, metrics are of particular interest that express the performance in regard to the reduction of inter-path, intra-path, and external interference eects as described in Section 2.2. In this section, we present performance metrics that assess the reduction of interference by measuring the gain in network performance with sequential and simultaneous throughput measurements of possible interfering links. Additionally, the network saturation is a feasible indicator of the increased network capacity. Also of interest is the introduced protocol overhead in form of exchanged control messages that is imposed on the network by the channel assignment algorithm. We describe each performance metric in detail and a concrete scenario to measure the metric on the DES-Testbed. Interference-free environment Several of the developed performance metrics are based on measurements in an interferencefree environment to determine the maximum throughput that is achievable on a link or path as a baseline. However, the DES-Testbed is distributed over several buildings with colocated networks operating in the same frequency spectrum. Therefore, the environment can not be guaranteed to be interference-free. The experiments for the channel conditions

64 48 Chapter 4. Methodology showed that there is no radio activity on most of the channels of the 5 GHz in the environment of the DES-Testbed (see Section 3.3.2). However, a signicant channel congestion has been determined for the channels of the 2.4 GHz spectrum. Therefore, we use the term interference-free in a way, that it comprises only the devices under our control, meaning that there is no additional radio activity from other testbed nodes during the measurement Intra-path Interference Ratio (IAR) The throughput of a multi-hop data ow may be reduced due to the impact of intra-path interference. The goal of the Intra-path Interference Ratio (IAR) metric is to assess, to what degree the intra-path interference eects have been reduced with channel assignment. The metric is calculated as follows. Let p u,v be the m-hop path, with m > 2, from node u to node v. Let L be the set of all links on p u,v. In order to measure the impact of the intra-path interference on p u,v, we perform the following two measurement steps. First, we measure the achievable throughput t seq l on each link l L sequentially, meaning only one link is active at a time. The aggregate throughput over all measurements expresses the maximum throughput that is available on this path in an interference-free environment and can be written as T seq = l L t seq l (4.1) As next, we measure the throughput of all links in l L simultaneously. The aggregate throughput over all measurements expresses the maximum throughput that is available on this path when all links are activated at the same time and thus exert the highest level of interference on each other. It can be written as T sim = l L t sim l (4.2) The intra-path interference ratio (IAR) metric is then dened as the ratio of the two aggregated throughput measurements with IAR = T sim T seq (4.3) A value for IAR close to 1 means that the aggregate throughput is not reduced when the throughput is measured on all links on p u,v simultaneously. In this case, the algorithm was capable to reduce the intra-path interference with the calculated channel assignment. A lower value for IAR expresses a higher impact of intra-path interference on p u,v. Breaking the path down and measuring the capacity of each link on its own has the advantage to avoid bottleneck eects. Otherwise, if we start a data ow from node u to node v over p u,v, the throughput on each link is limited by the throughput of the predecessor link. By considering each link on its own, we avoid the eect of such bottleneck links.

65 4.3. Channel Assignment Benchmark (CAB) 49 Figure : Example of the link selection for the IAR measurements. The path p consists of the following links: p = ((t9-149, t9-166), (t9-166, t9-169), (t9-169, t9-108), (t9-108, t9-007), (t9-007, t9-111)). Scenario After the channel assignment procedure, a path of length 5 is selected using the weighted cumulative expected transmission time (WCETT) routing metric [72]. WCETT considers the spectral diversity on a path rather than just the shortest path based on the distance in the communication graph, thus choosing edges with a high spectral diversity. Choosing the links on the path with WCETT, allows us to exploit the spectral diversity created by the channel assignment algorithms as much as possible. We then start the measurement procedure with all links on the selected path as described above. An example for such a selected 5-hop path is depicted in Figure Only the links on that particular path are displayed, each color of a link represents a dierent channel Inter-Path Interference Ratio (IRR) To measure the eect of inter-path interference, we select a subset L of all wireless links in the network with the additional constraint that every network node can only be adjacent to one link l L. This way, no multi-hop path is possible considering only the links in L. The links in L should be selected in a way that they are spatially close to each other, thus exerting a high degree of interference on each other when activated simultaneously. In a rst step, we measure the achievable throughput t seq l on each link l L sequentially, the aggregate throughput over all links is T seq = l L t seq l (4.4) In a second step, we activate all links simultaneously and measure the aggregate throughput which is T sim = l L t sim l (4.5)

66 50 Chapter 4. Methodology Figure : Example of the link selection for the IRR measurements. The example shows 10 links selected in the building of Arnimallee 6. The inter-path interference ratio (IRR) metric is then dened as the ratio between the aggregated throughput of sequential transmissions and concurrent transmissions. IRR = T sim T seq (4.6) The simultaneous transmissions will exert a maximum interference, whereas the sequential transmissions express the achievable throughput in an environment free of inter-path interference. Since each network node is participating in at most one trac ow, we measure the throughput reduction caused only by inter-path interference. A value for IRR close to 1 means that the aggregate throughput is not reduced when all links are activated simultaneously. Thus, the algorithm is capable to reduce the inter-path interference with the applied channel assignment. A lower value for IRR expresses that there is a higher impact of inter-path interference by multiple data ows. Scenario The DES-Testbed is distributed over four buildings on the computer science campus at Freie Universität Berlin. In order to ensure the spatial proximity when selecting a subset of 10 wireless links we constrain the choice of links on only one building. The mathematical institute located at Arnimallee 6 hosts 36 DES-Nodes. Considering these 36 nodes, we select 10 node-disjoint wireless links using the multi-graph reduction procedure as described in Section We then proceed with the measurement steps for the 10 selected links as described above. An example of the selected links for the measurements of IRR is depicted in Figure Only the links that have been selected for the measurements are displayed, each color of a link represents a dierent channel. However, it is of interest to investigate the assumption that the selected links indeed meet the criteria of close spatial proximity resulting in interference eects when the links are activated simultaneously. For this reason, we validated the assumption in an experiment

67 4.3. Channel Assignment Benchmark (CAB) 51 Figure : Eects of inter-path interference in a single channel network. In a single channel network, the achievable throughput when activating all 10 links at the same time is only about 40% in the median compared to when the links are activated sequentially. using one WNIC per node tuned to a global channel, resulting in a single channel network. We then measured the impact of inter-path interference as described above and replicated the experiment 40 times. The results for the IRR metric are depicted in Figure The plot shows that the achievable throughput when activating all 10 links at the same time is more than halved compared to when the links are activated sequentially. Therefore, there is a high impact of interference eects and our selection procedure is indeed capable of meeting the requirement of close spatial proximity Saturation Throughput Ratio (STR) The saturation throughput is described as the maximum load that the system can carry in stable conditions [110]. It can be determined by increasing the trac load on the system until the limit is reached. This metric can be used to directly show an increase of the network capacity gained with a channel assignment algorithm. For the measurement procedure, we dene L as the set of all wireless links in the network. In the rst iteration i = 0, we take a subset of k links S i=0 L with S i=0 = k and the constraint that each node can only be adjacent to one link l S i=0. We then measure the aggregate throughput of all links in S i=0 simultaneously which is SAT i=0 = l S i=0 t l (4.7) For the next iteration i = 1, we add another k links with the same constraints and repeat the measurement. The procedure terminates if SAT i+1 SAT i or S i cannot be extended any further. In other words, the total aggregate throughput of the network will cease to

68 52 Chapter 4. Methodology increase or no suitable links remain. The network is now saturated. Therefore we dene the saturation throughput S as follows: S = max(sat i ) (4.8) i In this way, we measure the absolute values for the saturation throughput and therefore, the results are specic to the testbed and lack interoperability. To solve this, we set the saturation throughput achieved by a channel assignment algorithm S CA in relation to the saturation throughput of a single channel assignment S Single. In other words, S CA will be normalized using a single channel assignment as a baseline and expresses how much the saturation throughput has increased over the single channel network. As performance metric, we use the saturation throughput ratio (STR) as follows STR = S CA S Single (4.9) A value of STR greater than one indicates an increase in the network capacity due to the channel assignment, i.e., STR = 2 means that the network capacity has been doubled compared to the single channel network. A value of STR close to 1 indicates that the algorithm has almost no impact on the network capacity. Scenario In order to measure STR on the DES-Testbed, we chose k = 10 for the step size. Thus, in the rst step we select 10 wireless links and measure the aggregate throughput of these links. For the next iteration we add another 10 links and repeat this procedure until the network is saturated or no more additional node-disjoint links can be found using the multi-graph reduction procedure as described in Section For the algorithms being compared, we take the maximum number of links that have been found with all algorithms in the particular iteration. An example of the maximum number of selected links is depicted in Figure Only the links that have been selected for the measurements are displayed, each color of a link represents a dierent channel External Interference Ratio (EXR) For channel assignment algorithms that consider external interference, a performance metric is required to estimate the performance of the algorithms in environments with external interference. We dene the performance metric as follows. First, we select a subset I of all network nodes N to act as interferers. With the number of interferers i = I, we are also able to control the level of interference created in the network environment by the interfering nodes. The activity of the interferers can vary from jamming one or more communication channels to using more WLAN specic bursty trac patterns. The remaining nodes M = N \ I run the channel assignment algorithm.

69 4.3. Channel Assignment Benchmark (CAB) 53 Figure : Example of the link selection for the STR measurements. The example displays all found node-disjoint links after the channel assignment procedure. For the performance evaluation, we select all links L in the network after the channel assignment with the additional constraint that every network node can only be adjacent to one link l L. Then, we measure the aggregate throughput of all links in L when they are activated simultaneously, which is T noif = l L t noif l (4.10) We repeat this measurement, but this time, the interferers I are active throughout the measurement. The aggregated throughput of the second measurements is dened as T if = l L t if l (4.11) The external interference ratio (EXR) metric is then dened as the ratio between the aggregated throughput while the interferers are active and when they are not, which is EXR = T if T noif (4.12) A value for EXR close to 1 means that the aggregate throughput is not reduced when the external interferers are active compared to when they are not. In this case, the algorithm avoids heavily congested channels and thus is not aected by the transmissions of the interferers. A lower value for EXR means that the transmissions of the interferers have decreased the aggregated throughput. Scenario In a rst step, we randomly select the subset I of all network nodes to act as interferers with I N, I = i. While the interferers are activated, the remaining nodes M = N \ I run the channel assignment algorithm. After the channel assignment procedure, we select the maximum number of node-disjoint links considering the nodes in M using the multigraph reduction procedure described in Section With the selected links, we start the

70 54 Chapter 4. Methodology Figure : Example of the link selection and interferers for the EXR measurements. The example displays the randomly chosen interferers depicted in red and the selected node-disjoint links after the channel assignment procedure. measurement procedure of EXR as described above. An example of the selected interferers and the selected links for the measurements is depicted in Figure The interferers are depicted as red nodes, each color of a link represents a dierent channel Protocol Overhead (PO) The overhead of a protocol is the additional amount of control messages the channel assignment approach imposes on the network. The control messages are exchanged between nodes, either on a regular basis or on demand. They are used for example to negotiate channel switches or to inform the neighboring nodes of changes in the channel assignment. As those control messages use the same communication paths as application data, they consume resources that would have been otherwise available to the latter. Consequently, the protocol overhead negatively aects the goodput of the network. Thus it is desirable, that channel assignment approaches have a minimum protocol overhead. A metric for protocol overhead has to consider the amount of control messages and the amount of nodes involved in exchanging those messages. As performance metric, we dene the protocol overhead (PO) as the mean number of messages sent by each node with PO = 1 N u N m u (4.13) where N denotes the network nodes and m u is the number of protocol messages sent by node u. Scenario The communication module of the DES-Chan framework as described in Section 6.2.4, allows to log all sent control messages during the execution of the algorithm. Thus, we can analyze the log le and determine the number of sent control messages per network node.

71 4.3. Channel Assignment Benchmark (CAB) 55 Metric Motivation Values Interpretation IAR Measure the reduction of [0, 1] The higher, the better - meaning a greater reduction of inter- intra-path interference eects. ference eects caused by intrapath interference. IRR Measure the reduction of inter-path interference eects. EXR STR PO Measure the reduction of interference eects caused by external interferers. Measure the network capacity compared to the single channel network. Protocol overhead imposed on the network by the algorithm. [0, 1] The higher, the better - meaning a greater reduction of interference eects caused by intrapath interference. [0, 1] The higher, the better - meaning the throughput is less aected by the radio activity of external interferers. [0, ] The higher, the better - meaning a higher network capacity has been achieved. [0, ] The lower, the better - meaning that less control messages have been sent per node. Table : Summary of the performance metrics of the channel assignment benchmark Summary of performance metrics All developed performance metrics for the channel assignment benchmark are summarized in Table The summary includes the motivation, possible values, and interpretation of each metric. Altogether, all sources of interference as described in Section 2.2 are considered Implementation CAB has been implemented primarily for the performance evaluation of channel assignment algorithms in the DES-Testbed. This section describes the architecture, workow, and challenges of the implementation. The core of CAB is a controller script written in Python. The controller runs on the DES-Portal, the testbed server which has a wired connection to all mesh routers. Additional external tools have been used, such as trac generators for throughput measurements, parallel-ssh 1 for remote command execution, and the ETX daemon of the DES-Chan framework to retrieve the network topology. Controller and workow The controller takes care of all required steps to execute the benchmark on the DES- Testbed. This comprises executing the channel assignment algorithms and the scenarios to measure the performance metrics, gathering and processing log les to extract results, and 1

72 56 Chapter 4. Methodology Algorithm 1 CAB workow 1: for rep in replications do 2: for alg in algorithms do 3: network.init() 4: alg.execute() 5: for sce in scenarios do 6: update topology() 7: sce.start() 8: end for 9: end for 10: end for a light-weight plotting engine based on matplotlib 2 to visualize the results. A conguration le allows to dene the algorithms and scenarios that should be evaluated. Additional meta data, such as the number of replications, the available network nodes and their WNICs, and log le folders, are specied in the conguration le as well. The simplied control ow for the channel assignment benchmark is given in Algorithm 1. Before the channel assignment algorithm is executed, all testbed nodes and the conguration of the wireless interfaces are reset. Then, the channel assignment algorithm is executed on the participating nodes. When all instances of the algorithm terminate with the nal assignment on all nodes or a timeout has been reached, the network topology is updated. Finally, the measurements for the particular scenario are started. Retrieving the network topology Several of the described performance metrics are based on measuring the throughput of a subset of all wireless links in the network. The benchmark controller therefore requires knowledge about the network topology. For this reason, the controller can trigger an update of the network topology realized with an ETX daemon, which detects all links in the network and their quality using the ETX link metric [100]. Based on the network topology, links can be selected under the conditions of the particular performance metric. Multi-graph reduction After updating the network topology, several of the introduced performance metrics require the selection of a subset of all links for the following throughput measurements. However, the selection process of these links yields several pitfalls and therefore requires careful consideration. Due to the broadcast nature of the wireless medium, it is very likely that the resulting communication graph in a multi-radio network after the channel assignment is a multi-graph with multiple links between nodes. Since we introduced the constraint for several metrics that the selected links are all node-disjoint (each node is adjacent to exactly one or none of the selected links) at most one link between any pair of nodes can 2

73 4.3. Channel Assignment Benchmark (CAB) 57 be selected. Selecting one of the links between two nodes randomly can lead to several problems due to the nature of many channel assignment algorithms. Several studies in the domain of channel assignment have emphasized that appropriate metrics on the upper layers, such as routing metrics, are required to exploit the benets of the spectral diversity. This leads to preferring links on a path with a higher spectral diversity than the shortest path between two network nodes. Several interference-aware routing metrics have been proposed that favor channel-diverse paths, such as WCETT [72], MCR [66], iaware [111], and EETT [112]. Additionally, several approaches exist for a combined solution of channel assignment and routing [113115]. The studies have shown that it is important to consider the spectral diversity in the link selection procedure in order to assess the potential performance gain due to the channel assignment. This consideration is especially important for channel assignment algorithms that rely on a global common channel in order to preserve the network connectivity. Using such a global channel has the eect, that if two nodes reside in each others communication range, they can most likely communicate since both nodes have an interface tuned to the global channel. Additional links between these nodes may exist if on each node at least one additional radio is tuned to another mutual channel. However, the proportion of links in the network on the global channel compared to links using channels other than the common global channel is very high. As an example, we investigate the results of the performance evaluation of the MICA and DGA channel assignment algorithms, which is described in detail in Chapter 7. The percentages of links tuned to the common global channel per algorithm are depicted in Figure For the randomized channel assignment approach RAND, 86% percent of all links are operated on the common global channel, 80% for DGA, and 74% for MICA. In the single channel network (SINGLE), all links are tuned to the global common channel. Picking links randomly is obviously a bad idea, since we can expect that most selected links are using the global channel. Instead, an interference-aware mechanism has to be used similar to the mentioned interference-aware routing metrics. A simple adaption of the interference-aware solution is to prefer links in the selection procedure, that use a channel other than the global common channel. This is also emphasized since the communication over the global channel is usually only intended to be used as a last resort, meaning only for nodes, that are not connected otherwise. Therefore, for the selection process, links that are not tuned to the global channels are preferred. Trac generators Throughput is one of the most used metrics to investigate the performance of wireless networks. In order to measure throughput, network trac has to be injected into the network. This can either be done by replaying previously recorded network trac, or trac generators can be used. These generators can be used to inject articial, customizable trac patterns into the network.

74 58 Chapter 4. Methodology Figure : Percentage of links tuned to the common global channel per algorithm. Two popular trac generators are iperf [116] and nuttcp [117]. Both tools are open source and allow to set several parameters, such as the transport protocol, message size, ow duration, and many more. iperf has certainly the larger user base and has been used to measure the performance of wired and wireless networks alike. iperf is based on a client-server approach and allows to measure the throughput between the client and server uni- and bidirectionally. Control message to signal the start and end of a trac ow are exchanged over the same connection, meaning there is no separate control channel for these messages. This can lead to problems in wireless environments with high level of interference when control messages are lost due to message collisions or bottlenecks. iperf tries to solve this problem by using an ACK mechanism. When no ACK message has been received for a particular control message, this message is rescheduled up to 10 times. nuttcp is a trac generator based on nttcp and ttcp, the latter was developed already in 1984 [118]. The feature set of nuttcp is similar to iperf. Additionally, nuttcp allows to specify a dierent communication channel for control messages other than the connection on which the throughput is measured on. This is especially useful for network architectures where wireless routers are also equipped with a wired network interface. The control messages can then be exchanged over the more reliable wired interface, while the actual throughput measurement is performed on the wireless channel. Most of the proposed performance metrics of CAB are based on throughput measurements in environments with a high level of interference. In order to measure the eciency of channel assignment algorithms, several measurements rely on simultaneous throughput measurements across the whole network. This may lead to high packet loss rates. If control packets are aected by the packet loss, the measurements are not successful and no statistics of the transferred trac can be made. For this reason, the idea to use a separate wired

75 4.3. Channel Assignment Benchmark (CAB) 59 Figure : Throughput results for a single link with iperf and nuttcp. control channel sounds attractive. Therefore, we investigate the impact of unsuccessful measurements due to lost control messages and if using a separate control channel is able to reduce the number of unsuccessful measurements. Experimental evaluation In a rst experiment, we compare the throughput measurements of a single link in an interference-free environment (see Section 4.3.1). Both trac generators are set to generate UDP trac with a data-rate of 6 MBit/s and a datagram size of 1024 Byte for a ow duration of 30 seconds. The wireless network interfaces corresponding to the link were set to a channel in the 5 GHz spectrum and the data rate was set to 6 MBit/s as well. The results for 100 throughput measurements are depicted in Figure The mean throughput over all measurements with iperf is 4.72 MBit/s compared to nuttcp with 4.74 MBit/s. All measurements have been successful in a way, that no control messages were reported as lost and for the measured throughput T holds T > 0.0 MBit/s. The results are in the expected order and the boxplots show, there is hardly any deviation across the measurements. As next, we investigate the performance of the trac generators in an environment with a high level of interference. For this scenario, we use 126 nodes of the DES-Testbed and set up one wireless interface per node to a common global channel in the 5 GHz spectrum. After the network initialization, we select as many node-disjoint links as possible, meaning that each network node is at most incident to at most one of the selected links. We then measure the throughput simultaneously on all selected links. The scenario is adapted from the saturation throughput ratio (STR) performance metric as described above. With 35 experiment replications, this results in a total of 4244 throughput measurements for both trac generators. Statistics about the measurements considering lost control messages and successful measurements with T > 0.0 MBit/s are given in Table For the results, we calculate the percentage of measurements in which control packets have been reported as lost. Additionally, we classify all measurements that reported a throughput T with T > 0.0 MBit/s as successful. Most notably, we observed that in about 15.7%

76 60 Chapter 4. Methodology Total ows Successful ows Lost control messages iperf (85.6%) 668 (15.7%) nuttcp (91.8%) 0 Table : Results of experiments with the trac generators iperf and nuttcp. The feature of nuttcp to use a dierent (wired) network interface for control message results in more successful measurements. of the measurements with iperf that control messages have been lost. The statistic is based on the exception thrown at the client, stating, that ACK messages have not been received after 10 tries. The number of unsuccessful measurements as reported by the iperf server is a little lower (14.4%) since these reports are generated independently of the client receiving the nal ACK at the end of the trac ow. In contrary, no control messages have been reported as lost for the measurements with nuttcp. The percentage of successful measurements is also signicantly higher with 91.8%. In summary, the experimental study shows, that the feature of nuttcp to use a dierent communication channel for control messages is capable of increasing the amount of successful measurements. This is especially useful for a network architecture such as the DES-Testbed, in which each wireless mesh routers has a wired connection to the testbed server. Thus, the wired interface can always be used as control interface for the throughput measurements with nuttcp. Based on the results of this study, nuttcp has been chosen as default trac generator for the channel assignment benchmark. However, iperf is still supported. The desired trac generator can be set in the conguration le of the channel assignment benchmark. Plotting engine CAB provides a light-weight plotting engine based on the Python plotting library matplotlib in order to visualize the processed results. Per default, results are plotted as boxplots, using 95% condence intervals, 25 and 75% quantiles, and whiskers. Results CAB has been used for the performance evaluation of the channel assignment algorithms described and developed in this dissertation. The results of studies on link-based and interface-based channel assignment are presented in Chapter 7, results of studies on externalinterference aware channel assignment are given in Chapter 8.

77 CHAPTER 5 Channel occupancy interference model The impact of interference on the performance of dierent protocols, such as channel assignment [41, 119], load balancing [120], and routing [72, 112] has been extensively studied. As a result, interference-aware approaches have been developed. The performance of these protocols depends heavily on the accuracy of the initial interference estimation for the network. It is therefore essential for the performance of these protocols to understand interference in order to derive models and procedures to accurately describe interference relationships between network nodes. In this chapter, a linear measurement-based approach for interference estimation in large-scale wireless mesh networks is presented. Based on this method for interference estimation, the channel occupancy interference model (COIM) is developed. The chapter concludes with the results of the validation of COIM in a study on the DES-Testbed. Excerpts of this chapter have been published in [50]. 5.1 Interference estimation Obtaining an accurate interference estimation for a wireless network deployment is not a trivial task. Due to the complexity of the radio signal propagation, initially simplied models based on heuristics have been proposed for the interference estimation. One of these is the point interference model, stating that two links interfere if they share a common endpoint [47]. A more sophisticated model denes for each node (or more precisely radio when nodes are equipped with multiple radios) a transmission radius r t and an interference radius r i, with r t < r i. A transmission is correctly received at node u, when only one node of all the nodes in the interference range of u transmits at a time. Models based on this assumption usually approximate the m-hop neighborhood of a node u as the interference set [48], which consists of all nodes whose transmissions may interfere at u. A typical value is m = 2, meaning the interference range of a node is twice its communication range. This model has been rst described as protocol model [35]. With these simple models, the interference estimation can be calculated without much eort based on the local neighborhood information of a network node. However, several studies have shown, that these simplications do not hold in reality. The predicted in- 61

78 62 Chapter 5. Channel occupancy interference model terference relationships are not accurate for indoor network deployments [46, 49, 50], the interference estimation is either too optimistic or pessimistic. For this reason, measurement-based methods have been proposed that promise a higher degree of accuracy. With pair-wise approaches, measurements are performed for each pair of network nodes resulting in a complexity of O(n 2 ) for a network with n nodes. One example for such an approach is the broadcast interference ratio (BIR) [46] which approximates the interference of unicast trac using broadcast transmissions. The study also showed that the carrier sensing (CS) mechanism of IEEE [121] is the main cause for the performance degradation, resulting in sender-side deferral of messages. Pair-wise measurements are also used in the creation of interference maps in [51]. Pair-wise approaches have been usually designed and evaluated in smaller testbeds or network deployments with about nodes [46, 51]. Although they deliver accurate results in these networks, they do not scale well for larger networks, since the measurements are based on trac ows with a xed duration that have to be carried out sequentially to avoid side eects. For example, if all nodes in a network are tested pair-wise, a network with 100 nodes would require 4,950 measurements. With each measurement lasting 20 seconds, the overall measurement procedure, in which the network can not be used for other applications, would take more than a day. Therefore, these approaches are not feasible for large or dynamic networks, in which the network topology changes over time. To address this issue, several approaches [73, ] proposed interference models that are seeded with only O(n) measurements. The measurement procedure is based on monitoring the received signal strength indicator (RSSI) values and the packet delivery ratio (PDR). However, this way only nodes are captured that are in each others communication range, since RSSI values can only be read from received packets. Therefore, nodes which are not in the communication range but in the interference range, also referred to as remote interferes [51], are ignored. In [122], the measurements are used to seed a physical layer model for message deferral and corrupted messages that considers single interferers. The approach has been extended to consider multiple interferers in [123] and [125]. Based on the correlation of the PDR and the signal interference to noise ratio (SINR) in IEEE networks, a physical model is proposed in [73]. 5.2 Channel occupancy measurements Our approach for interference estimation is based on measuring the channel occupancy (CO) using the carrier sensing (CS) mechanism of the wireless network interface while possible interfering nodes are utilizing the shared carrier. The channel occupancy (also referred to as channel load or channel busy fraction) is a value between 0 and 1, describing what fraction of a xed interval I the medium was sensed busy at a particular node. More formally, we dene CO k i as channel occupancy measured at node k while node i is sending at maximum rate in interval I

79 5.2. Channel occupancy measurements 63 T CO as threshold for the channel occupancy. If CO k i > T CO then the transmissions of node i have been sensed at node k CO represents an n n matrix for a network with n nodes and each value CO k i [0, 1]. A value of CO k i = 0.5 means, that at node k, the medium has been sensed busy for 50% of the measurement interval I in which node i was sending at maximum rate. We introduce the threshold T CO, because real-world network deployments are usually set up in unshielded environments. The environment may generate dynamic noise which occasionally exceeds the carrier sensing threshold. With the threshold T CO, we are able to discard a very low channel occupancy caused by the network environment. The procedure to obtain a reasonable value for T CO is described in detail in the next section. As described in Section 5.1, several approaches for interference estimation are based on measurements of the RSSI and PDR of each node's transmission, making them only capable to detect nodes in the communication range. With our approach we measure the channel occupancy instead, which has the advantage that also nodes in the carrier sense range are captured. However, measuring the channel occupancy poses new challenges, because it requires the availability of an unused radio channel in the network deployment environment. Otherwise, it is impossible with carrier sensing alone to distinguish between channel occupancy caused by the activity of another network node, by the environment, or external networks and devices. For instance, the DES-Testbed is deployed mostly in oce buildings, several WLANs are co-located that provide Internet access to students and the research sta. However, these WLANs operate mainly in the 2.4 GHz frequency band, but there is hardly any activity in the 5 GHz band, which can be used for the channel occupancy measurements Implementation With commodity IEEE hardware, we can measure the channel occupancy by directly accessing the hardware registers for carrier sensing of the wireless network interface card (WNIC). This way, we can monitor the activity on the medium for a specied interval and calculate the corresponding channel occupancy accordingly. The measurement procedure for CO in a network with nodes V is as follows. Node i broadcasts at maximum rate while all other nodes V \ {i} measure the channel occupancy. All nodes broadcast one at a time, which results in a complexity of O(n) measurements. In order to identify an unused channel for the measurements and to obtain a reasonable value for the threshold T CO, we execute the following sanity check procedure. On all channels of the 5 GHz frequency band, we measure the channel occupancy on all nodes simultaneously while our network is completely silent, i.e., no trac is generated. The results showed, that on the channels {36, 40, 48, 52, 56, 60, 64}, the measured channel occupancy varied between 0-0.7% for all nodes. This is low enough to qualify as unused, empty channels and allows us to choose a reasonable value for T CO. We set T CO = 0.01, meaning that the channel occupancy below 1% is considered as noise generated by the environment.

80 64 Chapter 5. Channel occupancy interference model Experiment Design For the measurement of the channel occupancy values in the DES-Testbed, we used one Atheros-based WNIC on each of the 128 network nodes. The data-rate of the WNIC is set to 6 MBit/s. On its turn, the sending node transmitted at full rate for 8 seconds (that is 6 MBit/s corresponding to the settings of the WNIC). All other nodes measured the channel occupancy with 10 samples, each sample capturing the channel busy time for an interval of 0.5 seconds. From these samples, we calculate the matrices for the mean sensed channel occupancy as CO Mean and the maximum as CO Max. With these settings, one execution of the experiment on the DES-Testbed with 128 network nodes lasted about 20 minutes. We run 50 replications of this experiment at night time resulting in a total runtime of roughly 17 hours Evaluation As expected, the fraction of results, in which the sending node has not been sensed by the listening node is the highest. In average over the 50 experiment runs, 90% of the 16,384 entries of both matrices CO Mean and CO Max are below the threshold T CO. This is as expected, since the DES-Testbed spans a large area comprising 3 buildings, so many nodes are located relatively far away of each other. The distribution of the values in CO Mean and CO Max for the remaining 10% (about 1,450 entries per experiment), in which the threshold T CO was exceeded, are depicted in Figure For the values in CO Max, we can observe an almost binary distribution, in which the vast majority of the entries are greater than 90%. For the values in CO Mean, we observe a higher number of values between 60 and 100%. Further investigation revealed that there is a correlation between the values in CO Mean, which are greater than 60% and the values in CO Max greater than 90%. Considering the median for each sender-listener combination (i, k) in CO Mean and CO Max over the 50 replications, we can derive that for about 90% of the entries it holds mean(co k i ) > 0.6 max(cok i ) > 0.9 (5.1) Taking a closer look at the values of CO Mean, we calculate the standard deviation of the measurements as depicted in Figure The plot shows, that there are many entries with a small standard deviation for CO k i < 0.1 and for CO k i > 0.9. However, for values with 0.1 < CO k i < 0.9, the standard deviation of the measurements is higher. This observation allows us to classify the sender-listener combinations (i, k) of the experiment. Using Equation (5.1), we can dierentiate between three classes: I S if mean(co k i ) > 0.6 max(cok i ) > 0.9 (i, k) I W if max(co k i ) > T CO (i, k) / I S I N if max(co k i ) T CO

81 5.2. Channel occupancy measurements 65 (a) Distribution of the entries with CO Mean > T CO. (b) Distribution of the entries with CO Max > T CO. Figure : Distribution of values in CO Mean and CO Max which are greater than the threshold T CO. While there is almost a binary distribution for the values in CO Max, most values in CO Mean are scattered between 60 and 100%. In other words, class I S comprises all combinations (i, k) in which node i is a strong interferer for node k, I W comprises the combinations in which node i is a weak interferer for node k, and I N comprises all combinations in which node i is considered not an interferer for node k. It is important to note that I N is the largest of these sets with 90% of the (i, k) combinations, I S comprises 9%, and I W only about 1%. We will use this observation for the development of an interference model in Section Correlation to network topology As next, we investigate the results for the correlation between the measured channel occupancy and the network topology based on the communication graph. We obtained the

82 66 Chapter 5. Channel occupancy interference model Figure : Standard deviation of the values in CO Mean over 50 experiment replications. network topology of the DES-Testbed using a neighbor discovery service which delivered all communication links between the network nodes and their corresponding link quality using the expected transmission count (ETX) metric [100]. For the evaluation, we constructed the communication graph with all detected links regardless of the link quality, which means that also very lossy links are included. The node degree based on the communication graph (the amount of 1-hop neighbors for each node), in relation to the node degree based on the channel occupancy measurements (nodes detected by carrier sensing), is depicted in Figure As can be seen, for all nodes, the amount of 1-hop neighbors in the communication graph is always less or equal to the number of nodes detected by carrier sensing. The mean node degree over all nodes derived from the communication graph is 5.40 compared to for the carrier sensing based node degree. This is an indication, that the proposed method is indeed capable of detecting nodes, that reside in the carrier sensing range of a node, but not in its communication range. It also shows, that the number of nodes outside the communication range but inside the carrier sense range is high, and therefore these remote interferers should not be ignored for the interference estimation. The CDF for the node degree based on the communication graph and the node degree based on the carrier sensing are depicted in Figure 5.2.4a and Figure 5.2.4b. The comparison in Figure 5.2.4c shows the correlation between the two measurements. The tted line shows, that we can predict the number of possible interferers based on the 1-hop neighbors quite well. This shows, that there is a correlation between the results of the channel occupancy measurements and the node degree based on the communication graph. As next, we take a closer look on the relation between the carrier sensing results of a

83 5.2. Channel occupancy measurements 67 Figure : Correlation of the node degree derived from the network topology and from the channel occupancy measurements by carrier sensing. sender-listener combination (i, k) and the distance in hops between the two nodes d i,k in regard to the network topology. For each node we determined all 1-hop neighbors, and checked how many of these neighbors have been successfully detected with carrier sensing, meaning that CO k i > T CO. We then did the same for the 2-hop, 3-hop,.., 6-hop neighbors. The results are depicted in Figure As expected, over all experiments, almost all 1- hop neighbors have also been detected by the carrier sensing. For the 2-hop neighborhood, only about 40% of the nodes have been detected by carrier sensing. This is an interesting observation, because according to our assumption, that means that about 60% of the 2- hop neighbors are not interferers. This corresponds to other validation studies that found that the 2-hop interference model is too pessimistic for real network deployments [46, 49]. Additionally, our results also show, that the 2-hop interference model is also too optimistic in certain cases, since still 10% of the 3-hop neighbors are still detected by carrier sensing. The results show once more the complexity of signal propagation modeling in indoor environments. This task is especially hard when additional dynamics such as moving people, open and closed doors and windows have to be taken into account as well. This underlines that measurement-based approaches are required in order to achieve more accurate results Measurements over time The DES-Testbed as the experimentation environment for the channel occupancy measurements is a static wireless mesh network, meaning that all nodes reside in a xed location. However, the environment of the DES-Testbed features a certain dynamic, since the nodes

84 68 Chapter 5. Channel occupancy interference model (a) CDF of node degree by network topology (b) CDF of node degree by carrier sensing (c) Comparison between node degree by network topology and carrier sensing Figure : Comparison of the CDF of the node degree based on the communication graph and the number of nodes detected by carrier sensing. are deployed in oces, seminar rooms, and lecture halls across dierent buildings. During day time, students might roam the buildings, research sta members might move nodes to slightly dierent locations or by accident move the antennas a little bit. Therefore, it is interesting to measure the channel occupancy at dierent times and compare the measurements in order to assess to what degree the interference patterns change over time. We chose three scenarios to compare the results of the measurements. First, we compare a measurement performed during day time with one performed at night. Second, we compare two measurement that are three days apart at night time, and nally, two measurements 6 week apart. The results of the error rate for each scenario is summarized in Table The error rate is dened as the percentage of entries in CO Mean (CO Max respectively) which are below the threshold T CO in one measurement and above in the other or vise versa. In other words, in one measurement the transmissions of a node have been sensed at another node

85 5.3. Interference model 69 Figure : Ratio of detected nodes via carrier sensing regarding the distance between the sender and listener in hops based on the communication graph. Time span between measurement Error rate CO Mean Error rate CO Max Day vs night 1.4% 2.1% 3 days apart 1.5% 2.1% 6 weeks apart 4.3% 6.6% Table : Error rates for CO Mean and CO Max for measurements over time. and in the other the transmissions have not been sensed. For the rst scenario, we observe an error rate of 1.4% for CO Mean and 2.1% for CO Max. We observe almost the same error rate for the measurements performed three days apart, both of them at night time. The error rates for the measurements six weeks apart are signicantly higher, with 4.3% for CO Mean and 6.6% for CO Max. The results show that for a static network in a mostly static environment, the patterns for the channel occupancy measurements are dierent at dierent times and the dierence increases with the time span between them. Therefore, the measurements have to be repeated periodically, stressing the importance of an ecient measurement procedure (i. e., in linear runtime). 5.3 Interference model Based on the analysis of the channel occupancy measurements, we dene the Channel Occupancy Interference Model (COIM). First, we dene the following l k,j direct link between nodes k and j

86 70 Chapter 5. Channel occupancy interference model tp k,j achievable throughput from node k to node j throughput from node k to node j with interfering node i sending simultaneously at maximum rate tp k,j i COIM is based on the following two assumptions: 1. CO k i < T CO CO j i < T CO tp k,j i = tp k,j (5.2) In other words, if CO k i and COj i do not exceed the threshold T CO, then the simultaneous transmissions of node i do not aect the capacity of the link l k,j. In this case, node i is not an interferer for the link l k,j. We will refer to this case as NO-IF throughout the remainder of this chapter. The second assumption complements the model with 2. CO k i T CO CO j i T CO tp k,j i < tp k,j (5.3) Meaning, that if CO k i or CO j i exceed the threshold T CO, we expect a decrease of the capacity of the link l k,j. In this case, node i is an interferer for link l k,j. We will refer to this case as IF. With these two assumptions, we dene the binary interference model COIM for a link l k,j and a possible interfering node i as follows. 0 if case NO-IF in Equation (5.2) holds COIM(l k,j, i) = 1 if case IF in Equation (5.3) holds In other words, if COIM(l k,j, i) = 1, then the node i is an interferer for the link l k,j, meaning that the transmissions of node i aect the capacity of the link l k,j. On the contrary, if COIM(l k,j, i) = 0, then the node i is not an interferer for the link l k,j. The choice for a binary model lies in the analysis of the results of measuring the channel occupancy, which showed, that 99% of the sender-listener combinations (i, k) are either part of the class of strong interferers I S or part of the class of non-interferers I N. Additionally, as our validation experiments for COIM show, interference eects are of a very dynamic nature in real network deployments, thus making it practically very hard to predict the eects of an interfering transmission on a ne-grained scale. 5.4 Validation We run the following experiment to validate the rst part NO-IF of COIM as described in Equation (5.2). We randomly selected 28 sample combinations, each existing of a link l k,j and an interfering node i. For each sample (l k,j, i), according to the assumption in NO-IF, for the corresponding values holds CO k i, COj i < T CO. Using our classication, this is equivalent to (i, k), (i, j) I N. Additionally, we introduce a further constraint regarding

87 5.4. Validation 71 the distance of the nodes k and i, in order to get more meaningful results. For each sample combination, we rst randomly select the interfering node i, and then select the link l k,j of all links of which CO k i, COj i < T CO and the distance d i,k between nodes i and k in hops based on the communication graph is minimal. More formally, for each sample (l k,j, i) holds CO k i, COj i < T CO min d i,k (5.4) k V\{i} With this constraint, node k is the closest to node i in regard of the distance in the communication graph for which node i is not an interferer. This way, we avoid samples, in which the adjacent nodes of l k,j are relatively far away of i regarding the network graph and interference eects are unlikely to appear. As a result of applying this additional constraint, for all sample combinations the distance is d i,k = 2, meaning that the interferer i resides in the 2-hop neighborhood of the node k. Thus, the results of this experiment will give us also insights about the performance of the 2-hop interference model, since according to the 2-hop model, all sample combinations should suer from interference. For the validation of the second part IF of COIM as described in Equation (5.3) we randomly select 28 sample combination (l k,j, i) in which we expect that node i is an interferer to the link l k,j. Therefore, for each sample (l k,j, i), according to the assumption in IF, for the corresponding channel occupancy values holds CO k i T CO. For both experiments, the following sequential steps are executed for each sample combination. 1. Measure the throughput tp k,j for a xed interval of 10 seconds. We use iperf to generate an UDP ow with a data-rate of 6 MBit/s. 2. Start sending at maximum rate at node i and measure the throughput tp k,j i with the same parameters as in step 1. We use a UDP trac generator based on Python Twisted [126] for the transmissions of node i. Both experiments were repeated 35 times for each sample combination, leading to a total runtime of 16 hours. For each experiment replication, we are interested in the ratio of the achievable throughput when the interferer i is active simultaneously with the ow on link L k,j and when it is not. More formally, that is expressed by RTP k,j i = tpk,j i (5.5) tp k,j If the transmissions of node i do not aect the capacity of l k,j, the measured throughputs should be the same and RTP k,j i will be close to 1. On the contrary, we expect values for RTP k,j i of less than 1, if the node i is an interferer for the link l k,j. Since link qualities and signal propagation are known to be dynamic over time in unshielded testbed environments, we use the following threshold to quantify interference. We dene that node i is an interferer to l k,j if RTP k,j i < On the contrary, node i is not an interferer to l k,j if RTP k,j i 0.85.

88 72 Chapter 5. Channel occupancy interference model Figure : Aggregated results for the experiments to validate COIM. For the validation of NO-IF the results over all sample combinations are in the median close to 1, whereas the results for IF are close to 0.4. The aggregated results over the two experiments for the validation of COIM are shown in Figure It can be seen that for NO-IF the results over all sample combinations are in the median close to 1 and the low height of the boxplot shows that the deviation is small. The results for IF are close to 0.4 in the median, which means, that the impact of interference on a particular link can severely aect the capacity of that particular link. For the median, the expected throughput is less then half of what can be achieved in the absence of the interfering node. The results for each sample combination for the validation of NO-IF are depicted in Figure 5.4.2a and the results for IF in Figure 5.4.2b. For both graphs, it can be seen by the hight of the boxplots that the deviation over the 35 replications for each sample is quite high which reects the dynamic link and signal propagation characteristics in real unshielded networks. In the case of NO-IF, the median of RTP k,j i 0.85 for all sample combinations, meaning that we successfully validated this part of COIM. This allows us also to estimate the accuracy of the 2-hop interference model, since the distance between the possible interferer i and node k is d i,k = 2. According to the 2-hop interference model, in all sample combinations interference eects should have been appeared. Since this is not the case, the 2-hop model is obviously too pessimistic in our testbed, and most likely for other network deployments as well. For IF, the median of RTP k,j i < 0.85 for all sample combinations but one, meaning that the interference eects of the simultaneous transmissions of node i do have an eect on the capacity of the link l k,j for all but one sample combinations. The results show, that COIM is able to predict if a node i is an interferer for a link l k,j and will decrease the capacity for that particular link. However, we are not able to quantify the impact of the

89 5.4. Validation 73 (a) Validation of NO-IF. In samples with a resulting RTP k,j i i is not interfering with the link l k,j. < 0.85 for the median, node (b) Validation of IF. In samples with a resulting RTP k,j i is not interfering with the link l k,j for the median, node i Figure : Results for the validation of COIM. The results for each sample combination and the corresponding throughput ratio RTP k,j i are plotted.

90 74 Chapter 5. Channel occupancy interference model COIM Prediction NO-IF IF Observed Interference 0 28 Observed No-Interference 27 1 Table : Prediction table for the validation of COIM. simultaneous transmissions of the interfering node. As seen in the results, the medians of RTP k,j i are quite evenly distributed between 0.26 and Both results according to the predictions of COIM are summarized in Table Relation to channel assignment The study has shown, that COIM provides a high degree of accuracy for the interference estimation. In a next step, we will compare the impact of channel assignment on the performance in regard of the utilized interference model. Since a measurement-based approach provides a more accurate interference estimation than simple heuristics, we expect that the performance of channel assignment algorithms can be increased by using more accurate interference models. By executing the same algorithm with dierent interference models, we are able to quantify the performance gain that can be credited to a more accurate models.

91 CHAPTER 6 A framework for distributed channel assignment algorithms An important part of this dissertation is the evaluation of channel assignment algorithms in a real network environment. For this reason, implementation candidates for the experimental environment in question, the DES-Testbed, have to be developed. This chapter introduces DES-Chan, a software framework that provides common services and data structures for channel assignment algorithms to support the researcher in the development process. Excerpts of this chapter have been published in [127, 128]. 6.1 Motivation Several distributed channel assignment algorithms have been evaluated in small wireless testbeds [36,48,65,66]. However, an experimental study is lacking that compares the performance of multiple dierent algorithms in a large-scale wireless testbed. One of the reasons is the huge eort it takes to develop an implementation candidate of such an algorithm for real network environments. The development process yields several challenges and pitfalls: The researcher has to deal with operating system specics, drivers for the wireless interfaces, and the capabilities and limitations of the particular hardware. Additionally, common operating systems such as Linux and Windows do not support multi-channel protocols out of the box. Furthermore, if more than one particular algorithm should be studied, common problems and services have to be addressed multiple times. Such a common service for example is the interface management, which is needed in order to carry out channel switches and to congure the wireless network interfaces. Additionally, distributed channel assignment algorithms usually require information about the local network topology and the channel selection therein. This task can be solved by providing a neighborhood discovery service that allows to retrieve all direct neighbors. By providing such services to the developer, a research framework for channel assignment algorithms can signicantly speed up the development process. 75

92 76 Chapter 6. A framework for distributed channel assignment algorithms Based on these observations, we developed the Distributed, Embedded Systems - channel assignment framework (DES-Chan) with the following key features: Abstraction layer: DES-Chan provides an abstraction layer in order to hide operating system details from the developer, such as memory management and handling the wireless interfaces. Another important part of distributed channel assignment algorithms are communication protocols to exchange information about the local channel selection and to negotiate channel switches. A framework can provide an API for these tasks and can handle sending and reception of messages. Thus, the overall development eort can be signicantly reduced for the researcher. Service library: Despite the diverse strategies of channel assignment algorithms, there are several key services that are required by most algorithms. Therefore, the framework should provide a set of such common services as derived from the analysis of distributed channel algorithms discussed in Section 2.5. By providing such common services, the development eort can be signicantly reduced and the framework enables rapid prototyping of implementation candidates for new algorithms. Modular architecture: A modular architecture is the foundation for an extensible framework. The framework should not limit the developer to the already provided services, since further channel assignment algorithms may require additional services. By encapsulating the functionality in modules, further services can be added on demand to the framework. DES-Chan oers two main contributions to the research community. First, the eort to develop implementation candidates for channel assignment algorithms is signicantly reduced. Due to the abstraction layer and the provided services, ideas for channel assignment strategies can be quickly implemented as a proof of concept in testbed environments. Thus, a wide range of dierent algorithms can be implemented and validated in a real network environment. Second, with the reduced development eort, diverse algorithms can be evaluated and their performance can be compared in the same experimentation environment. Such a study can deliver insights into the feasibility of dierent strategies. DES-Chan has been developed as a software framework written in Python. The framework was designed so that it does not require any changes of the Linux kernel or the wireless network interface drivers. Therefore, modications of the wireless network interface drivers, for instance to reduce the channel switching time, are not part of the framework. DES-Chan is therefore easy to integrate into existing wireless mesh network testbeds. 6.2 Services and architecture DES-Chan comprises a set of data structures and services useful for channel assignment algorithms. The data structures provided by DES-Chan comprise graph representations of the local network topology and the corresponding conict graphs [1, 52].

93 6.2. Services and architecture 77 Before we describe the services in detail, we summarize their functionality as follows: Interface handling: This basic service allows to change the wireless network interface settings, for example to carry out channel switches. Neighborhood discovery: This service retrieves information about the local network topology and the channel selection therein. This information is usually needed as input for channel assignment decisions in distributed algorithms. Topology monitoring: A topology monitoring service periodically assesses the local network topology and its state. Neighbors, links, and their respective quality can be monitored which enables the algorithms to become adaptive by reacting to topology changes with rening the channel assignment. Message exchange: A message exchange service for node to node communication between the instances of an algorithm can be used to disseminate the current channel selection and to negotiate channel switches. Interference modeling: Interference models are required to estimate the local interference. The models range from simple heuristics such as the m-hop neighborhood to measurement-based approaches [46, 122]. Spectrum sensing: With spectrum sensing, the conditions of the available channels can be measured. Based on the measurement data, channels may be blacklisted for the channel selection. The architecture of DES-Chan comprising all modules is depicted in Figure DES- Chan exposes the oered services and data structures to the channel assignment algorithms. As future algorithms will require additional services, DES-Chan has been designed to be extensible using a modular architecture. As next, the services are described in detail Interface management The interface management module provides wrapper functions to set the conguration of the available wireless network interface cards. The congurations are carried out with Python WiFi [129], a library based on the Linux Wireless Extensions. First, the interface can be tuned to a specic channel which is a crucial requirement for all channel assignment algorithms. Additionally, the following settings can be changed: ESSID, cell id, IP address, network mask, data rate, and the transmit power. Further helper functions allow to retrieve the current state of an interface and check for unused interfaces that are still available Neighborhood discovery The neighborhood discovery module determines the local network topology by assessing all links and their quality for each network node based on the expected transmission count

94 78 Chapter 6. A framework for distributed channel assignment algorithms Channel Assignment Algorithm DES-Chan Message Exchange Interface Management Neighborhood Discovery Topology Monitoring Interference Models Spectrum Sensing Python Twisted Python WiFi ETX Daemon 2-hop... DES-Sense wlan0 wlan1... Figure : Architecture of the DES-Chan framework. DES-Chan provides common services and data structures for distributed channel assignment algorithms. (ETX) link metric [100]. The ETX link metric estimates how many transmissions for a packet are required until it is successfully received. ETX values are calculated by each node sending broadcast probes and logging how many probes from its neighbors were successfully received. The forward and reverse delivery ratio are then used for the calculation of ETX, because for unicast communication in IEEE , an ACK frame has to be successfully received at the sender. The ETX value for a link is then calculated as ETX = 1 d f d r (6.1) where d f is the forward delivery ratio and d r the reverse delivery ratio. The ETX implementation etxd for the DES-Chan framework has been realized as a Linux daemon. The default settings for etxd are summarized in Table The values can be congured via command line arguments. The probe interval is set to 1 second. The link quality values are averaged over a moving window, which is set to 30 seconds per default, and thus spans 30 probe intervals. The payload of a probe contains all neighbors and the statistics about the received probes from this neighbor according to the window size. The payload length is thus variable, for each neighbor 14 Bytes of data are appended to the probe. The daemon sends UDP probe packets periodically to the broadcast addresses while simultaneously listening for incoming probes of its neighbors. In order to always provide upto-date information, etxd dynamically adapts its conguration if network interfaces have been recongured, shut down, or brought up. This is achieved by checking periodically for

95 6.2. Services and architecture 79 Setting Probe interval Probe payload Moving window size Description 1s Varying - depends on the number of direct neighbors. 30s Table : Default conguration parameters for the etxd daemon of DES-Chan. changes in the interface congurations. The information of the local network topology can be queried by channel assignment algorithms via an inter process communication (IPC) interface that can be accessed via sockets. A simple, textual protocol is used to retrieve the neighbors of a node, as well as the quality and the channel the corresponding link is operated on. The daemon can be queried with an optional parameter regarding a minimum link quality. If used, only those neighbors are returned, which are reached via links for which the ETX value is not higher than the supplied minimum link quality. In addition to the link quality, etxd also returns the current channel of the WNIC that is used to reach the respective neighbor. Instances running on other network nodes can also query etxd to get all neighbors of a neighbor. Thus, by querying all direct neighbors, each node can retrieve the local 2-hop neighborhood, which is quite commonly used for interference estimation [35] Topology monitoring The ETX daemon etxd is also the foundation of the topology monitoring service. Via the IPC interface, channel assignment algorithms can query the state of the local network topology periodically and react adaptively to changes. Such topology changes can be caused by the arrival of new nodes or the absence of previously detected nodes. The information about the changes can be used to update the local interference estimation. The local interference level can be calculated repeatedly and may lead to further netuning of the channel assignment decisions Message exchange Node to node communication is an important part of distributed channel assignment algorithms. Messages are exchanged for example to disseminate the current channel assignment of a node. Also, many algorithms require communication protocols to negotiate channel switches in a synchronized manner in order to avoid channel oscillation or dead interfaces [36, 48]. The used communication protocols and message formats can dier signicantly depending of the particular channel assignment algorithm. Thus, a exible networking library is needed, that allows implementing various protocols with few eort. The Python Twisted [126] library serves as the foundation for the node to node communication. The library provides an asynchronous networking engine and hides technical details like creating sockets and establishing connections from the developer. The researcher can

96 80 Chapter 6. A framework for distributed channel assignment algorithms quickly develop the required protocol implementation for exchanging messages among the network nodes, for instance in order to propagate changes in channel assignment or to carry out three-way handshakes prior to the actual channel switch Interference models The DES-Chan framework supports the implementation of various interference models. Currently, the 2-hop interference model and the channel occupancy interference model (COIM), as described in Chapter 5, are available. Based on the 2-hop interference model, for a particular node u, all nodes are regarded as interferers that have a distance of 2-hops or less to u regarding the network topology. The implementation of the 2-hop heuristic supports a binary notion of interference, i.e., two links either interfere or do not. Additionally, a ne-grained notion taking the spectral distance into account is supported [48]. Equation (6.2) shows the cost function I for two center frequencies f 1 and f 2. The additional parameter α denotes the minimum frequency dierence of orthogonal channels. 0 if f 1 f 2 α I(f 1, f 2, α) = 1 ( f 1 f 2 ) α otherwise (6.2) By setting α to 30 MHz in the interference cost function I, three channels of the IEEE b/g 2.4 GHz frequency band are theoretically orthogonal, i.e., {1, 7, 13}. For COIM, the results of the measurements are stored on each network node. Each row comprises the measured channel occupancy at a testbed node while another node was sending at full capacity. The DES-Chan module for COIM accesses the database on demand to retrieve the corresponding values for the channel occupancy measurements. Based on the values, the channel assignment algorithm instance on one particular node can determine if another node is an interferer and if so, the channel selection of that node should be considered for its own channel assignment decisions Spectrum sensing Spectrum sensing is an ecient method to measure the load on the available channels and thus detect the radio activity of external networks and devices. Especially with the rising number of wireless devices in the unlicensed frequency spectrum, solutions to counter the spectrum scarcity problem is becoming more important [130]. DES-Chan provides the DES-Sense module to carry out measurements of the channel load on specied channels. While the spectrum sensing method is describe in detail in Section 8.2, we describe here how the channel assignment algorithms can measure and retrieve the channel load values. The DES-Sense module is a software-based spectrum sensing solution and consists of the following two components: Sensing component - The sensing component is a daemon that periodically measures the channel load based on the carrier sensing statistics of the wireless network card.

97 6.2. Services and architecture 81 Based on the statistics we are able to calculate the channel busy time, in which the wireless medium could not have been used for wireless transmissions. The sensing component can be congured dynamically with the set of channels C = {c 1, c 2,.., c k } that will be monitored and the duration T = {t 1, t 2,.., t k }, each channel is monitored. IPC interface - For the integration into DES-Chan, an inter process communication (IPC) interface is provided that allows algorithms to retrieve the measured statistics about the channel load. The results of the most current measurements can be queried via the IPC interface so that the channel assignment algorithms can fuse the statistics into their channel assignment decision. The particular channel assignment algorithm, can decide if the channel load is only measured prior to the execution of the algorithm or also during its operation. Since the network nodes are not equipped with a dedicated interface for the spectrum sensing measurements, one interface per node can not be used for other trac during the measurement phase Data structures DES-Chan provides data structures and functions for graph representation to model the network graph. A data structure for the network graph contains a vertex for every network node and an edge for every link between two network nodes. The data structure allows to store a list of channels for each edge, thereby supporting multiple links between node pairs. Functions are provided to access the vertices and edges, for instance to update the channel selection. Graph objects can be stored in the DOT format, that can processed to generate an image of the graph [131]. A function provides the possibility to merge two graph objects together by creating the union of both vertex sets and edge sets. This is useful, if the 2-hop neighborhood of a particular node is constructed by querying the direct neighbors for their local network topology information. Additionally, a corresponding data structure for conict graphs is provided. The conict graph is constructed from the network graph and the specied interference model. Each edge of the network graph is a vertex in the resulting conict graph. An edge between two vertices of the conict graph exists, if the corresponding links interfere with each other according to the applied interference model. The data structure for conict graphs provides the corresponding transformation methods for these actions. It allows to manipulate the channel of the corresponding edge in the network graph and automatically updates the interference information in the conict graph. The conict graph data structure can be easily extended to implement other concepts, such as multi-radio conict graphs [53].

98 82 Chapter 6. A framework for distributed channel assignment algorithms Figure : Snapshot of the visualization tool DES-Vis. The screenshot displays the network topology of the DES-Testbed. Links are displayed in dierent colors depending on the channel they are operated on. 6.3 Channel assignment visualization A visualization of the channel assignment procedure and the corresponding network topology can be helpful in analyzing the performance of channel assignment algorithms. In order to visualize the channel assignment, we extended DES-Vis, the 3D-visualization tool of the DES-Testbed Management system (DES-TBMS) [96]. DES-Vis visualizes the state of the network based on the DES-Mon network monitoring tool. For each experiment, a monitoring interval can be set, in which DES-Mon gathers snapshot-based the state of each network node. The data that is retrieved by DES-Mon has been extended with the information of the local network topology as assessed by the ETX daemon etxd. This data comprises for each node the links to all direct neighbors, including the channel the link is operated on. Thus, for each snapshot, the global channel assignment is retrieved. Based on this information, the DES-Vis was extended to display links in dierent colors depending on the utilized channel. This way, a graphical representations of the channel assignment can be displayed and a rst insight of the achieved channel diversity is possible. An example of the visualization of the network state is shown in Figure Extensibility The modular architecture of DES-Chan allows an easy integration of additional services. While core services such as the interface management and the message exchange module will most likely stay untouched, alternatives for other modules may oer interesting insights. Especially the availability of dierent interference models allows to compare the performance in terms of the accuracy of the interference estimation. Currently, DES-Chan comprises the 2-hop interference model and the measurement-based channel occupancy interference model (COIM). Evaluating channel assignment algorithms with dierent algorithms may deliver insights on the potential of more accurate interference models. Such an evaluation is presented later in Section 7.5.

99 CHAPTER 7 A study of link-based and interface-based channel assignment In this chapter, we present a study of the performance of existing distributed channel assignment algorithms in a large-scale network environment. As rst, we describe two distributed algorithms in detail with the focus on the developed implementation candidates based on DES-Chan. A performance evaluation of these algorithms in a large-scale wireless network follows. As part of the study, the performance of the algorithms is also compared to a randomized approach and the single channel network. The chapter closes with a discussion of the results and an outlook towards the external-interference aware channel assignment. Excerpts of this chapter have been published in [119]. 7.1 Motivation The motivation of this experimental study is to investigate the performance gain with distributed channel assignment in large-scale networks. We compare the performance of two promising approaches from literature that have achieved good results in smaller testbeds with less than 15 network nodes. Both approaches are distributed algorithms that optimize the channel assignment in the local neighborhood of each network node in a simple greedy manner. However, there is a fundamental dierence in how channels are assigned. The minimum interference channel assignment (MICA) algorithm [36] assigns channels to links. This link-based strategy has the benet that the channel assignment is independent of the overlaying routing algorithm because all links in the single channel network also exist in the multi channel network. The distributed greedy algorithm (DGA) is an interface-based approach [48, 62], in which channels are assigned to interfaces. The connectivity of the network is preserved by one dedicated interface per node operating on a common global channel as a fall back if no other link on a dierent channel is available. The interface-based approach promises to be more robust in dynamic topologies than the link-based approach, albeit the dedicated interface can not be used for channel assignment. 83

100 84 Chapter 7. A study of link-based and interface-based channel assignment Both algorithms have been evaluated by their authors in small wireless testbeds (with 11 and 14 multi-radio nodes) and produced signicant improvements in regard to the network capacity. The experimental study in this chapter is guided by the following three questions: Do the reported performance results in small wireless testbeds also hold in a largescale wireless testbed with more than 100 nodes? Based on the performance evaluation with the channel assignment benchmark, is one of the two strategies more favorable in large-scale wireless networks? Are such simple greedy strategies suitable as a foundation for our envisioned externalinterference aware channel assignment? We rst describe both algorithms and their implementation candidates for the DES- Testbed based on DES-Chan followed by the performance evaluation of the algorithms based on the channel assignment benchmark (see Chapter 4). 7.2 Minimum Interference Channel Assignment (MICA) Idea The minimum interference channel assignment (MICA) assigns channels to links instead of interfaces and is therefore topology preserving [36]. In other words, all links in the single channel network, also exist in the multi channel network after the channel assignment procedure. This way, the approach is independent of the overlaying routing algorithm. The authors formulate the channel assignment problem as minimizing the total network interference by minimizing the number of edges in a conict graph [1,52]. The problem is reduced to the Max K-cut problem [63], thus proving that it is NP-hard Algorithm The proposed distributed algorithm is based on a greedy approximation algorithm for the Max K-cut problem given in [63]. The distributed MICA algorithm has been presented in detail in [36]. At the network initialization, all links are assigned to the same channel. After retrieving the local communication graph of the m-hop topology (with m 1) and constructing the conict graph V c, each nodes tries to minimize the local interference. Each node iterates over all link-channel combinations (u, c), identifying the combination which results in the largest decrease of interference in the local neighborhood. The largest decrease is achieved with the combination (u, c) that removes most edges in the local conict graph V c compared to all other combinations. The interface constraint has to be respected, which means that no more channels can be assigned to a node than it has interfaces. In order to avoid channel oscillations, additional constraints for the channel switches have been introduced. A node may only switch the channel of a link that is incident to that particular node. Additionally, a network link

101 7.2. Minimum Interference Channel Assignment (MICA) 85 between two nodes is owned by the node with the higher ID and only this node may assign a channel to the link. The node ID can be any unique identier, such as IP address or MAC address. In summary, in each iteration at node n, the link-channel combination (u, c) is selected, that fullls all of the following criteria Node n is incident to the link u Node n owns link u, meaning that its ID is higher than the ID of the other node incident to u Applying channel c does not violate the interface constraint on both nodes The combination (u, c) has not been tried before at node n The combination (u, c) results in the highest decrease of interference in regard to the number of edges removed in the conict graph V c If no further (u, c) combination can be found that matches all criteria, the algorithm terminates. Once a combination (u, c) is found, node n sends a ChannelRequest message to the corresponding neighbor m for the link u. If changing the channel on u to c does not violate the interface constraint, node m replies with a ChannelReply message and switches the channel accordingly. Otherwise node m does not reply to the ChannelRequest message. After receiving the ChannelReply message, node n changes the channel as well and sends an ChannelUpdate message to all of its m-hop neighbors, so they can update their local conict graphs. If the ChannelReply message has not been received at node n until a timeout, node n discards the combination (u, c) and proceeds with the next iteration. The pseudo code for the distributed MICA algorithm running on one particular node is given in Algorithm 2. Additionally, asynchronous events may occur, for instance when a node receives a ChannelRequest message from a neighbor. These cases are described in the next section with a ow diagram of the implementation candidate in Figure The termination of the algorithm is ensured with the constraint that each link-channel combination (u, c) can only be tried once. This also gives insights into the runtime of the algorithm. The total number of iterations (over all instances of the algorithm) is O( V c K), where V c is the number of vertices in the conict graph (of the whole network) and K is the number of available channels Implementation A rst implementation based on DES-Chan has been created in the scope of a master thesis in [132]. The implementation candidate comprises two main classes for the algorithm and the communication between individual instances: MICA and Messaging. Since the approach is a distributed algorithm, it is executed concurrently on every node. The following description refers to an instance running at one specic node. Before the algorithm is started, the single channel network is initialized by setting one of the network interfaces to

102 86 Chapter 7. A study of link-based and interface-based channel assignment Algorithm 2 Distributed MICA running on node n Require: local network graph V, local conict graph V c 1: S all possible link-channel combinations (u, c) 2: repeat 3: minimum 0 4: for (u, c) in S do 5: if n not owner(u) then 6: continue 7: end if 8: if switching u to c violates the interface constraint then 9: continue 10: end if 11: if f I (u, c) > f I (minimum) then 12: // interference reduction with (u, c) is larger than current best choice 13: minimum (u, c) 14: end if 15: end for 16: if minimum > 0 then 17: send ChannelRequest to corresponding neighbor of u 18: if ChannelReply received then 19: switch u to c, update V c 20: send ChannelUpdate to m-hop neighborhood 21: end if 22: S S \ (u, c) 23: end if 24: until minimum = 0 the common global channel. An instance of the MICA class is created and initialized with an empty conict graph and a new Messaging object. From that point on, the program ow is determined by a series of events, which are depicted in the ow diagram in Figure The Messaging object listens for incoming messages from other MICA instances, and uses the Neighborhood Discovery module from DES-Chan to retrieve the initial network graph. The local conict graph, which is still empty at that point, is updated with the information of the network graph. Afterwards, the ndminimum method is called. The method determines the link-channel combination (u, c) that leads to the largest decrease in interference. If such a link-channel combination is found, a ChannelRequest message is sent to the corresponding neighbor of link u. If no combination could be found that further reduces the interference, the algorithm terminates. After the ChannelRequest message has been sent, the program ow is stalled until a message from the corresponding MICA instance is received or a timeout is reached. The reply can either be a ChannelReply or a ChannelReject message. We introduce the ChannelReject message, which is not part of the original algorithm, in order to avoid long waiting times if the combination (u, c) violates the interface constraint at the receiving node. If the ChannelRequest is answered with a ChannelReply, the originator of the request applies the new channel to the particular link. To make sure that the channel

103 7.2. Minimum Interference Channel Assignment (MICA) 87 start received ChannelRequest initialize single channel network apply choice send ChannelReply Yes obeys interface constraint update conflict graph No abandon choice find minimum send ChannelUpdate send ChannelReject received ChannelReject found minimum Yes No stop apply choice received ChannelReply send ChannelRequest Figure : Flowchart of the distributed MICA implementation. Methods of the MICA class are colored light-blue, receiving and sending messages are represented by light-orange parallelograms. can actually be switched to when the ChannelReply message is received, the program had allocated a lock for the unused interface before sending the ChannelRequest. Thereby it ensures, that the interface is not used to satisfy a ChannelRequest from another neighbor, while waiting for the corresponding ChannelReply message as described below. After receiving the ChannelReply, the node applies the pending channel switch and sends a ChannelUpdate message to its m-hop neighbors which update their local topology information. If the channel request is answered with a ChannelReject message, the respective link-channel combination is removed from the set of possible combinations. It will not be considered again by the ndminimum method, which ensures the termination of the algorithm. After the requested channel has been either approved or rejected, the current network topology and the local conict graph are updated accordingly. Afterwards, the next iteration is started until the interference cannot be decreased any further. During the execution of the algorithm, ChannelRequest messages from other instances may be received asynchronously. When receiving such a ChannelRequest, the node checks whether it is able to change the channel without violating the interface constraint. If the corresponding network interface is only used for communicating with the sender of the request, the interface constraint is respected and the link can be switched to the new channel. If, however, other neighbors are connected via the same network interface, the proposed channel switch would eliminate these links. In that case, an unused network interface has to be set up and tuned to the respective channel. If there is an unused interface available, or no other links would be aected by the channel switch, the node sends a ChannelReply message, indicating that the channel can be changed. After the reply has been sent, the node congures its interfaces according to the new assignment.

104 88 Chapter 7. A study of link-based and interface-based channel assignment In contrast, if the channel switch would aect links to other neighbors and no unused interfaces are left, the request is answered with a ChannelReject message and the linkchannel combination is discarded Deviations from the original algorithm The implementation varies slightly from the original algorithm. Explicit ChannelReject messages have been introduced in the presented implementation. When a node receives a channel request, which cannot be fullled because of the interface constraint, it replies with a ChannelReject instead of not replying at all. Thereby, long waiting times are avoided and the node can immediately continue with the next iteration Feasibility of link-based channel assignment Fractional Network Interference (FNI) MICA has been evaluated in a 11 node testbed by the original authors. The evaluation included a graph-theoretical analysis based on the fractional network interference (FNI). The FNI is dened as the ratio of the number of edges of the conict graph of the single channel network and the number of edges in the conict graph after the nal channel assignment. More formally, the FNI is dened as FNI = E CG CA E CGSingle (7.1) with E CGCA as the number of edges in the conict graph after the channel assignment, and E CGSingle as the number of edges in the single channel network. The authors reported for a network with 3 wireless network interfaces per node a FNI of about In our experimental evaluation (see Section 7.5), the FNI for MICA over 35 experiment replications is shown in Figure The median over all experiment replications for the FNI is about 0.5, meaning that the amount of interference relationships has been halved due to the channel assignment procedure. However, the value is also signicantly higher than what the authors reported for a similar setup. The reasons for this deviation are the following. First, the authors calculate the graph-theoretic FNI from generated static topologies without using a network simulator or testbed for the evaluation. In our experiments, the network topology is not static and the quality of links varies over time, even if we only consider high quality links for the channel assignment procedure. For this reason, links may break or new links may emerge during the channel assignment procedure. From the log les of the execution of MICA, nodes have been removed from the set of interferers (and neighbors) or added at a later point of time. If a MICA instance does not have complete and accurate knowledge of the neighbors and interferers of a node, the results are likely to be not optimal. Second, the network density of the DES-Testbed has been increased with the latest testbed extension. Additional DES-Nodes have been placed in the already covered build-

105 7.2. Minimum Interference Channel Assignment (MICA) 89 Figure : Fractional Interference Ratio (FNI) for MICA. The median of the FNI over all experiment replications is about 0.5. ings, thus there are several nodes with a relative high node degree higher than 15. With the increased network density, the number of edges in the conict for the single channel network increases as well. Still, the number of wireless interfaces for the DES-Nodes has not changed. For this reason, the majority of edges in the conict graph adjacent to a highly connected node can not be removed. These reasons can explain, while the values for the FNI in our experimental study are higher than the ones originally presented in the paper. Observed Issues The analysis of the rst results with the MICA algorithm in the DES-Testbed revealed some interesting observations. The rst issue arises from the fact that the original algorithm considers only one link between two nodes in the network. Nodes are either connected or not, but multiple links between the same nodes using dierent radios are not taken into account. The algorithm is topology preserving, thus links should neither break or emerge during the execution of the algorithm. However, during the channel switch procedure, an implicit link may be created, that is not considered by MICA. For example in Figure 7.2.3, after retrieving the initial network topology, the algorithm changes the link (BC) to channel 36. However, both nodes are connected via the interface tuned to channel 14, which is still used to communicate with their neighbors A and D respectively. Therefore, an implicit link on channel 14 exists between B and C. In the view of the algorithm the edges in the conict graph in Figure 7.2.3c shown as dotted lines have been removed with the channel switch. But since the resulting network graph is a multi-graph, these edges in the conict graph still exist and an interference-aware routing has to be used in order to not utilize the implicit link. These implicit links violate the feature of the algorithm that it is topology preserving in a way that it is transparent for routing algorithms. Another issue arises from the assumption of link stability in real wireless networks. For the channel assignment procedure, the authors consider only stable links with a packet deliver ratio (PDR) of at least 80%. However, we observed that some wireless links are

106 90 Chapter 7. A study of link-based and interface-based channel assignment D D AB BC B 14 C 14 B 14 C BC CD A (a) Initial network topology A (b) Channel switch for link BC (c) Resulting conict graph Figure : Implicit links with MICA. Starting from the initial network graph using channel 14 in (a), the link BC has changed to channel 36 in (b). In order to preserve the links AB and CD, a previously unused interface has been used to apply the new channel assignment, which leads to an implicit link on channel 14 between the nodes B and C. In the view of the algorithm the edges in the conict graph in (c) shown as dotted lines have been removed with the channel switch. very dynamic even in stationary wireless mesh networks such as the DES-Testbed [91]. Their quality may change over time and they exhibit dierent characteristics on dierent channels. Thus, existing links may break apart and new links may emerge during the course of the algorithm. Since the algorithm does not account for that, the resulting channel assignment may be sub-optimal. In the worst case, channels are assigned to links which temporarily do not exist and thus can not be used. As a solution, the current network topology could be updated periodically to notice recent changes. Considering only high quality links also has the consequence that poorly connected nodes may not be reached. With a growing network size, the probability rises that nodes exist on the network edge that are connected via lossy links. Selecting only high quality links for the channel assignment procedure may thus exclude a fraction of nodes. The already lossy links may break if nodes change their channels, resulting in network partitioning, which is clearly not desired. As a solution, the constraint for the link quality can be relaxed to also take lossy links at the network edge into account. 7.3 Distributed Greedy Algorithm (DGA) Idea The distributed greedy algorithm (DGA) assigns channels to network nodes, or more precisely, to network interfaces [48,62]. Intuitively, each node tries to minimize the interference

107 7.3. Distributed Greedy Algorithm (DGA) 91 by assigning the least used channels in its interference set. The interference set of a node n consists of all nodes and their channel assignment whose transmissions aect sending and receiving at node n. To preserve the network connectivity, one interface on every node is operating on a global common channel and not used for channel assignment. For any additional interfaces (referred to as switchable interfaces), the channel is determined with the following algorithm Algorithm At the network initialization, one dedicated interface is set to the common global channel. After retrieving the local communication graph, the interference set of each node is determined, which is approximated with the m-hop neighborhood (with m = 3 in the original publication 1 ). With an interference cost function f I as given in Equation (7.2), the spectral overlap of two channels is used to determine the level of interference in the interference set. The cost function takes the center frequencies f 1 and f 2 of two radio channels and the additional parameter α into account. α denotes the minimum frequency dierence of orthogonal channels. A value of f I = 0 means that the channels are orthogonal, meaning they do not interfere with each other. f I (f 1, f 2, α) = max(0, α f 1 f 2 ) (7.2) Using the interference cost function, each node tries to assign the channel that results in the lowest level of interference in its interference set to its switchable interfaces according to Algorithm 3. In each iteration, the algorithm considers exactly one switchable interface and calculates the interference cost for all available channels K with the channel assignment of the interference set. The results is the channel c best with the lowest overall interference cost. If c best is dierent than the currently assigned channel, a channel switching procedure is initiated. As an additional constraint, only channels can be assigned that are used by at least one neighboring node, in order to avoid isolated interfaces. The channel switching procedure is based on a 3-way handshake with the nodes in the interference set to synchronize channel switches [133]. When a node wishes to change a channel, it sends a ChannelRequest message to all nodes in its interference set. The request contains the old channel for the interface in question, the new channel the node intends to assign, the achieved reduction of interference, and the channel assignment of the receiving node. The latter is used to verify, that the channel reduction has been calculated based on the correct assumptions of the channel assignment in the interference set. When receiving a ChannelRequest message, a node puts a possible own channel switch intent on hold until the ongoing procedure is nished. The recipients of the ChannelRequest reply with a ChannelAccept message, if the proposed channel switch does not conict with their own intended channel switches. The originator of the request applies the channel switch, 1 The results of the experiments with the channel occupancy interference model in Chapter 5 showed that considering the 3-hop neighborhood is too pessimistic for the DES-Testbed.

108 92 Chapter 7. A study of link-based and interface-based channel assignment Algorithm 3 Channel selection with DGA for an interface n i at node n Require: S n Interference set of node n K Set of available channels c j channels assigned to each node j S n c ni current channel of the interface n i 1: F best j S n f I (α, c ni, c j ) 2: c best c ni 3: for k in K do 4: F(k) j S n f I (α, k, c j ) 5: if F(k) < F best then 6: F best F(k) 7: c best k 8: end if 9: end for if all nodes of the interference set reply with a ChannelAccept message. A following ChannelUpdate message is sent to the nodes in the interference set to conrm the channel switch. If the ChannelRequest conicts with another pending channel switch, a node replies with a ChannelReject message. When receiving a ChannelReject message, the originator of the request aborts the current channel switching procedure by sending a ChannelAbort message to all nodes in its interference set. The algorithm terminates, when the local interference can not be reduced any further. The authors prove the convergence of the algorithm by showing that the overall network interference decreases monotonically with each channel switch [62]. It has to be stated though, that this does only hold in static network topologies Implementation A rst implementation based on DES-Chan has been created in the scope of a bachelor thesis in [134]. Similar to MICA, the implementation candidate of DGA is distributed into two main classes. The DGA class comprises the channel selection algorithm while the Messaging class contains the implementation of the communication protocol for channel switch synchronization. When the algorithm is started, one interface on each network node is set the common global channel to create a single channel network. The switchable interfaces are initialized by selecting a channel randomly from the set of available channels. As next, information about the local 2-hop neighborhood of each node is retrieved with the Neighborhood Discovery service of the DES-Chan framework using the ETX metric [100]. Once the local topology has been retrieved, the interference set is created by querying all 2-hop neighbors for information about their current channel assignment. This completes the initialization after which the program ow is determined by a series of events, which are depicted in the ow diagram in Figure First, the channel selection algorithm for one of the switchable interfaces is executed. If a better channel than the current one is found, the node generates a ChannelRequest and

109 7.3. Distributed Greedy Algorithm (DGA) 93 start initialize single channel network query m-hop neighbors update interference set wait for decision apply choice send ChannelAbort find best channel discard own request No send ChannelAccept No send ChannelUpdate abort request found best channel No stop send ChannelReject Yes own request wins Yes conflict with own request received all ChannelAccepts received ChannelReject Yes wait for timeout received ChannelRequest send ChannelRequest Figure : Flowchart of the distributed DGA implementation. Methods of the DGA class are colored light-blue, receiving and sending messages are represented by light-orange parallelograms. calculates an exponentially distributed delay before initiating the channel switch procedure. During the waiting time, ChannelRequests of other nodes may be received and answered accordingly with a ChannelAccept or ChannelReject. A ChannelReject is sent, if the received request either has false assumptions of the node's current channel assignment or the request conicts with another one and has a lower priority. The priority of a request is determined by the reduction of interference it achieves according to the interference cost function. If two competing request achieve exactly the same level of interference reduction, the request originated by the node with the higher ID has the higher priority. Otherwise, the incoming request is replied with a ChannelAccept and the node awaits the nal decision of the channel switch procedure. The channel switch procedure is successful, if the originator of the request receives ChannelAccept messages from all nodes in its interference set. Finally, a ChannelUpdate message is sent to the nodes in the interference set and the channel switch is applied. If a ChannelReject message has been received or a timeout is reached, the channel switch procedure is aborted. A ChannelAbort message is sent and the next iteration of the algorithm begins Deviations from the original algorithm The original specication of the algorithm in [48] considered two network interfaces per node resulting in exactly one switchable interface per network node. While the authors do not restrict their approach to network nodes equipped with exactly two radios, several

110 94 Chapter 7. A study of link-based and interface-based channel assignment unclear issues arise when nodes with additional radios are considered. Same channel restriction: First, it is obviously not desired that multiple switchable interfaces are tuned to the same channel. To prevent that a node assigns the same channel to more than one interface, we introduce an additional constraint. Initial channel selection: The channel selection algorithms limits the available channels to only those, which are used by at least one of the direct neighbors of a node. An important question is the initial channel selection for the switchable interfaces. Obviously, they can not be initialized using the common channel, because no channel diversity would be possible. We solved this issue, by randomly assigning channels to the switchable interfaces before the rst call of the channel selection algorithm. Interface priority: In each iteration of the channel selection algorithm, only one interface is considered. The order of the interfaces in which they are considered for the channel assignment calculation is not specied. The priority of the interfaces can either be solved in a round robin manner, or greedily considering additional parameters. For instance, the algorithm can prioritize the interface that is tuned to the most used channel in the interference set, for which a channel switch would most likely lead to the largest decrease in interference. Interference cost functions for multiple bands: In the original algorithm description, the common global channel was chosen from one frequency band (2.4 or 5 GHz) and the available channels for the switchable interface were limited to the channels of the other band. With multiple switchable interface, selecting multiple channels of one frequency band can not be avoided. The experiments investigating the adjacent channel interference (ACI) revealed that dierent values for α at 2.4 and 5 GHz are valid in the DES-Testbed (30 and 60 MHz, respectively). These values have to be considered when the interference level is determined with the interference cost function. The implementation candidate has been adapted with the required changes Feasibility of interface-based assignment An interesting aspect of the algorithm is the relatively high protocol overhead the algorithm imposes on the network. In contrast to MICA, where only the two nodes incident to the link in question negotiate a channel switch, all nodes of the interference set participate in the channel switch negotiations. The 3-way handshake with all nodes of the interference set is required to guarantee the convergence of the algorithm. Verifying the channel assignment of the interference set before applying channel switches ensures that the decisions are based on the correct assignment. This way, semi-optimal decisions based on incomplete or obsolete information are avoided.

111 7.4. Random channel assignment (RAND) Random channel assignment (RAND) The random channel assignment (RAND) algorithm realizes a simple random strategy that functions as follows. One interface per node is set to a common global channel in order to preserve the network connectivity. Any additional interface on a network node is set to a random channel of all available channels in the 2.4 and 5 GHz frequency spectrum. However, we introduce the additional constraint that any two interfaces on a node do not use the same channel. A randomized channel assignment algorithm has been often used in order to compare its performance to other more sophisticated approaches. However, depending on the network topology and the number of available channels, such a randomized approach may already be capable to increase the network capacity signicantly. As an example for such a strategy, the approach in [69] uses a randomized component when it is guaranteed that two nodes operate on a mutual channel. 7.5 Performance evaluation The performance evaluation has been carried out with the channel assignment benchmark (CAB) described in Chapter 4. The DES-Testbed functioned as experimental platform Experiment settings and runtime The following algorithms have been evaluated in this experimental study: MICA, DGA, RAND, and the single channel network (SINGLE) as a baseline. The settings and parameters for all experiments are given in Table One experiment replication consisted of measuring the performance metrics of the channel assignment benchmark IAR, IRR, PO, and STR for all algorithms in question. Additionally, we discuss the resulting network topology after the channel assignment and the channel distribution of the wireless links. First, we compare the algorithms utilizing the 2-hop model for interference estimation. In a second step, we compare the performance of DGA utilizing the 2-hop model and the channel occupancy interference model (COIM). The runtime for one experiment replication with all scenarios of CAB was about 160 minutes. With a total number of 35 replications, the whole study took about 93 hours Network topology and link distribution An snapshot of the network topology after the channel assignment procedure for each algorithm is depicted in Figure For the single channel network (SINGLE) in Figure 7.5.1a, all links are operated on the default channel. In the resulting network topologies for all other algorithms, links are also operated on other channels while the default channel is still predominant. For DGA and RAND the reason is that one interface per node is permanently tuned to the default channel in order to preserve the network topology. MICA

112 96 Chapter 7. A study of link-based and interface-based channel assignment Parameter Values Comment Number of nodes 126 DES-Nodes Each node is equipped with one IEEE b/g radio and two IEEE a/b/g radios. Experiment replications 35 Each replication ran in 160 minutes, resulting in a total experiment duration of about 93 hours. Algorithms MICA, DGA, The single channel network (SIN- RAND, SINGLE GLE) functions as a baseline. Algorithm runtime 20 minutes Most instances completed the assignment in that time. Available channels 36, 44, 48, 52, 60, 64, 100, 108, 112 This applies to MICA and DGA. The randomized approach uses all channels of the 2.4 and 5 GHz bands. Default channel 14 Applies to MICA, DGA, and RAND. The single channel network is initialized on this channel as well. Interference model 2-hop model, COIM COIM has been developed in the scope of this dissertation and is described in Chapter 5. Trac generator nuttcp See Section Performance metrics IAR, IRR, SAT, PO Additionally, we consider the nal network topology and channel usage distribution. Table : Experiment settings for the comparison of channel assignment algorithms. starts with setting up one interface per node on the default channel as well, but allows to change this channel for a link, if a pending channel switch would not cause losing the direct connection to a neighboring node. This means in practice, that only nodes with a node degree of 3 or less are able to switch the default channel to another one because otherwise links will break. This is because of the interface constraint, which states that each node can only assign as many channels as it has network interfaces. Since the average node degree is about 6 for all experiment replications, most nodes operate the initial interface permanently on the default channel as well. As next, we investigate the spectral diversity created by the algorithms considering the number of links of the resulting network topology. The number of unidirectional links per 1 algorithm in the network graph with a quality of ETX 0.8 is depicted in Figure 7.5.2a. The plot shows, that all channel assignment algorithms were able to increase the number of links compared to the single channel network. In the single channel network, 600 links have been established in the median of 35 experiment replications. For RAND, 710 links have been determined, 750 links for DGA, and 780 for MICA. This is as expected, since all algorithms preserve the network topology, and use the multiple WNICs to create additional

113 7.5. Performance evaluation 97 (a) The single channel network (SINGLE) (b) Final channel assignment with MICA (c) Final channel assignment with DGA (d) Final channel assignment with RAND (e) Channel color legend Figure : Snapshot of the network topology of the single channel network (SINGLE) after the channel assignment for MICA, DGA, and RAND.

114 98 Chapter 7. A study of link-based and interface-based channel assignment (a) Number of uni-directional links per algorithm with 1 ETX 0.8. (b) Percentage of links operating on the default channel. Figure : Amount of unidirectional links in the network and the percentage of links operating on the default channel. Figure 7.5.2a shows that compared to the single channel network (SINGLE), the number of links for all algorithms in question was increased using additional WNICs on dierent channels. The percentage of links on the default (or common global) channel is lowest for MICA and DGA as shown in Figure 7.5.2b. links between network nodes on dierent channels. Overall, the number of links in the resulting network topology has been increased by about 18-30% depending on the channel assignment algorithm. This matches also the percentage of links on the common global channel per algorithm as depicted in Figure 7.5.2b. For RAND, 86% of all links in the resulting network topology have been operated on the default channel in the median, 80% for DGA, and 74% for MICA. In summary, the network topology after the execution of MICA features the lowest percentage of links on the default channel. This can be credited to the fact, that nodes are able to leave the default channel in a channel switch, if no other links are aected in the process Channel distribution As next, we take a closer link on the channel distribution of all links in the network topology other than the ones operated on the default channel. The frequency of the remaining unidirectional links per channel over all experiment replications for MICA, DGA, and RAND is shown in Figure For MICA and DGA, links have been established on all available channels and the frequency of most channels is in the same order. This can be credited to the random elements of the algorithms. For MICA, the order of the link-channel combinations (u, c) is randomly selected in each call of the findminimum function. This way, when multiple channels would lead to the same decrease of interference, a random channel is chosen. For DGA, the initial channels for the switchable interfaces are selected randomly. Channel switches are restricted to those channels, that are already used by at least one direct neighbor in order to avoid dead interfaces. However, few channels have

115 7.5. Performance evaluation 99 (a) Channel distribution for MICA (b) Channel distribution for DGA (c) Channel distribution for RAND Figure : Channel distribution of links in the network topology that are not operated on the default channel for MICA, DGA, and RAND. The frequency of links per channel is derived from all 35 experiment replications. not been used as much as others with DGA, in particular channels 44, 108, and 60. This can be credited to an implementation detail of DGA, in which the set of available channels is not randomized before nding the least interfering channel for an interface. This way, the order of channels is always the same, and channels with a lower index in the set of available channels are assigned more often if multiple channels achieve the same reduction of interference according to the used interference model. An interesting observation can be made considering the channel distribution resulting

116 100 Chapter 7. A study of link-based and interface-based channel assignment from the RAND algorithm. While there are also links established on all available channels, the amount of links using channels on the 2.4 GHz spectrum is higher than for the channels on the 5 GHz spectrum. This can be explained with the dierent signal range for the Atheros-based WNICs in the two dierent frequency bands as we have investigated in Section Since we have not adjusted the transmit power depending on the utilized frequency band, the same network interface has a larger radio signal range when operated on 2.4 GHz compared to 5 GHz. This leads to a slightly higher frequency of links for the channels on the 2.4 GHz band compared to the 5 GHz band Intra-path Interference Ratio (IAR) The results for 35 replications regarding the intra-path interference ratio (IAR) and the relative gain compared to the single channel network are shown in Figure In each replication, we selected a 5-hop path randomly based on the WCETT routing metric [72]. We then measured the achievable throughput on all links sequentially and while all links have been activated simultaneously. The ratio of the two measurements is dened as IAR (see Section 4.3) and the results are shown in Figure 7.5.4a. Additionally, we compare the results of the single channel network (SINGLE) with the results of the channel algorithms in question by calculating the relative performance gain for the channel assignment algorithm as shown in Figure 7.5.4b. For the single channel network, in the median of all experiment replications, only about 20% of the throughput has been preserved when all links are activated simultaneously compared to when activated sequentially. For the RAND algorithm about 40% of the throughput is preserved, which means that the achievable throughput compared to SIN- GLE has been doubled as depicted in Figure 7.5.4b. DGA preserves about 50% of the throughput, resulting in 2.5 times the throughput of SINGLE. MICA performs best with achieving almost 60% of the throughput when the links are activated simultaneously, thus being able to triple the throughput of the single channel network. In summary, the results show that intra-path interference eects are extremely harmful to the network performance. In a single channel network, only 20% percent of the maximum throughput on a 5-hop path are achieved when the links are activated simultaneously. The results are partly caused by the setup of the DES-Nodes, and more specically, with the small distance of the of the wireless network interfaces and antennas which results in board crosstalk and radiation leakage as described in Section The experiments on adjacent channel interference (ACI) indicated severe performance degradation if multiple WNICs are operated on the same network node simultaneously on the same frequency band. Compared to the single channel network, the channel assignment algorithms have been able to leverage the decrease in throughput of simultaneous transmissions signicantly. Even the randomized approach (RAND) is able to double the throughput compared to the single channel network. DGA further increase the performance gain and MICA performs best, the achievable throughput triples compared to the single channel network. In con-

117 7.5. Performance evaluation 101 (a) Results for the intra-path interference metric (IAR). (b) Performance gain compared to the single channel network. Figure : Results for the intra-path interference metric (IAR) and the relative performance gain compared to the single channel network. sequence, both channel assignment algorithms DGA and MICA signicantly reduce the eects of intra-path interference Inter-path Interference Ratio (IRR) The results for the inter-path interference performance metric (IRR) and the relative gain compared to the single channel network are depicted in Figure In each replication, we selected a subset of 10 node-disjoint links of all nodes deployed in Arnimallee 6. We then measured the achievable throughput for all links sequentially and while all links have been activated simultaneously. The ratio of the two measurements is dened as IRR (see Section 4.3) and the results are shown in Figure 7.5.5a. Additionally, we compare the results of the single channel network (SINGLE) with the results of the channel algorithms in question by calculating the relative performance gain for the channel assignment algorithm as shown in Figure 7.5.5b. For the single channel network, only 30% of the throughput could be preserved when all links are activated simultaneously. With RAND, about 75% percent of the throughput has been preserved, meaning that the achievable throughput in this scenario has been doubled compared to the single channel network. Both algorithms MICA and DGA perform best, preserving about 95% of the achievable throughput. Compared to the single channel network, the throughput was almost tripled with both algorithms. The results show, that both algorithms MICA and DGA are eciently reducing almost all eects resulting from inter-path interference in this particular scenario. Both algorithms are close to achieve the maximum throughput, thus eliminating all interference eects. The results are better than the ones for the IAR metric, since we only consider node-disjoint links in this scenario (see Section 4.3). This way, only one radio interface per node is activated throughout one experiment replication, which eliminates board crosstalk or radio leakage eects on a single DES-Node.

118 102 Chapter 7. A study of link-based and interface-based channel assignment (a) Results for the inter-path interference metric (IRR). (b) Performance gain compared to the single channel network. Figure : Results for the inter-path interference metric (IRR) and the relative performance gain compared to the single channel network. Compared to the single channel network, the channel assignment algorithms are capable to increase the throughput signicantly in scenarios exposed to inter-path interference. The randomized approach (RAND) is able to double the throughput compared to the single channel network. DGA and MICA both triple the results of the single channel network and outperform RAND Saturation Throughput Ratio (STR) The results for the saturation throughput ratio (STR) and the absolute network capacity are shown in Figure To determine the network capacity, we selected 10 wireless links of all links in the testbed in the rst step and measured the aggregate throughput of these links. For the next iteration we added another 10 links and repeated this procedure until the network was saturated or no more additional node-disjoint links could be found as described in Section For all algorithms, at least 40 node-disjoint links have been found in all replications and the results for STR are shown in Figure 7.5.6a. Additionally, the absolute network capacity achieved by each algorithm is shown in Figure 7.5.6b. For RAND, the network capacity has been doubled compared to the single channel network. With DGA the network capacity has been tripled, and MICA performs best with increasing the network capacity by 310%. A similar pattern is presented considering the absolute network capacity. The network capacity of the single channel network reaches 43 MBit/s. RAND achieves almost 90 MBit/s, DGA 130 MBit/s, and MICA performs best with almost 140 MBit/s. In summary, the channel assignment algorithms are able to signicantly increase the network capacity compared to the single channel network. While the randomized approach (RAND) doubles the network load compared to the single channel network, MICA and DGA achieve thrice the network load of the single channel network.

119 7.5. Performance evaluation 103 (a) Results for the saturation throughput ratio (STR). (b) Absolute saturation throughput in MBit/s over 40 links. Figure : Results for the saturation throughput metric (STR) and the absolute saturation throughput considering 40 links Protocol Overhead (PO) The results for the protocol overhead (PO) that is imposed on the network by the algorithm are shown in Figure The plot shows the mean number of control messages sent per node during the channel assignment procedure over all experiment replications. With DGA, the mean number of messages sent by each network node is about The number of sent messages for MICA is signicantly lower with only about 200 messages per node. The reason for this gap can be found in the dierent communication protocols for channel switches. With MICA, the overhead is relatively low, since only the owner of a link can initiate a channel switch for this particular link. Additionally, only the two nodes adjacent to the link in question negotiate the channel switch with a three-way handshake. For a successful channel switch, only the ChannelRequest and ChannelReply messages are exchanged between the corresponding nodes, followed by possible ChannelUpdate messages. DGA functions dierently in that manner. In order to ensure convergence, the communication protocol involves all nodes of the 2-hop neighborhood of the node that initiates the channel switch procedure. The desired channel switch is announced to all nodes of the 2-hop neighborhood and all nodes must agree with a ChannelAccept message before the channel switch can be carried out. If at least one node sends a ChannelReject message, the procedure is aborted and a new round of the algorithm begins. Unsuccessful channel negotiations were common in the channel assignment procedure in this experimental study. The reason for this is two-fold. First, due to the dynamic network topology, the nodes do not always have complete and correct information of the interference set. If false assumptions for a pending channel switch are detected, the procedure is aborted. Additionally, a channel request is rejected, if another node is waiting to issue a channel request with a higher priority. Since the priority is only checked upon the reception of a channel request, many channel requests do not survive the conict resolution phase.

120 104 Chapter 7. A study of link-based and interface-based channel assignment Figure : Protocol overhead of MICA and DGA. The plot shows the mean number of sent messages per node over all experiment replications. 7.6 Impact of the interference model For the results presented so far, both algorithms MICA and DGA have been congured to use the 2-hop interference model. We have shown in Chapter 5 that the channel occupancy interference model (COIM) determines interference relationships more accurately than the 2-hop interference model. For this reason, we investigate the impact of the utilized interference model on the performance gain in this last series of experiments by comparing the performance results of DGA utilizing the 2-hop and COIM interference model. As performance metric, we only consider the saturation throughput ratio (STR), since the metric gives an insight in the achievable network capacity. First, we investigate the impact of the interference model on the interference set of the network nodes. The mean number of interferers for each network node in regard to the interference model is shown in Figure With the 2-hop interference model, the mean number of nodes in the interference set is about 11, while it is 4 with COIM. The results are in line with the analysis of COIM as described in Section 5.4. The analysis revealed that the 2-hop interference model is too pessimistic in the DES-Testbed, since only about 40% of all 2-hop neighbors are actually interferers for a particular node. The results according to the STR performance metric are shown in Figure As also observed in Section 7.5.6, DGA with the 2-hop interference model increases the network capacity by about 290% compared to the single channel network. When DGA uses COIM instead, the network capacity is even further increased to 310%. These observations also hold for the absolute throughput measurements in Figure 7.6.2b. Thus, as expected, the channel assignment algorithm benets from a higher accuracy of the interference model. However, the increase of the network capacity is only marginal when utilizing COIM instead of the 2-hop model. The observations in the channel distribution in Section showed, that DGA hardly assigns the last two channels in the set of the available channels.

121 7.6. Impact of the interference model 105 Figure : Mean number of interferers according to the utilized interference model. (a) Results for the saturation throughput ratio (STR). (b) Absolute saturation throughput in MBit/s over 40 links. Figure : Results for STR and the absolute saturation throughput according the utilized interference model with 10 available channels. For this reason, the pessimistic 2-hop interference model may achieve good results albeit the high number of nodes in the interference sets. We assume that DGA utilizing COIM instead can cope better with a smaller number of available channels. Therefore, we repeat the experiment with a reduced channel set comprising only 6 channels instead of 10. The results for STR and the absolute throughput over 35 replications are shown in Figure The results for DGA with COIM are similar to the previous ones with 10 available channels. The network capacity could be increased by about 310% compared to the single channel network. When DGA utilizes the 2-Hop model, the network capacity could only be increased by about 260%. As expected, the impact of the reduced set of available channels is higher when DGA utilizes the 2-hop interference model compared to COIM. In summary, the results show that channel assignment algorithms benet from more accurate interference model, especially when the number of available channels is limited. With the trend of spectrum scarcity due to the increasing number of wireless devices in the unlicensed frequency bands, accurate interference models gain more importance.

122 106 Chapter 7. A study of link-based and interface-based channel assignment (a) Results for the saturation throughput ratio (STR). (b) Absolute saturation throughput in MBit/s over 40 links. Figure : Results for STR and the absolute saturation throughput according the utilized interference model with 6 available channels. 7.7 Discussion In this chapter we presented a performance evaluation of distributed channel assignment algorithms in large-scale wireless mesh networks. Two existing approaches have been implemented based on DES-Chan and evaluated with the channel assignment benchmark (CAB). The performance evaluation showed that even a simple randomized strategy already delivers good results, being able to double the network capacity compared to the single channel network. Both algorithms MICA and DGA outperform the randomized approach, achieving thrice the network performance compared to the single channel network. MICA creates a slightly higher spectral diversity as DGA (see Section 7.5.3), which also reects in the performance evaluation with slightly better results. The algorithms also differ in the protocol overhead they impose on the network. The number of control messages is signicantly higher with DGA to ensure the convergence of the algorithm. Furthermore, we have shown that the performance in regard to the network capacity could be further increased with the usage of accurate interference models. Compared to the 2-hop interference model, the performance results are better when COIM is utilized, especially in environments with a limited set of available channels. In summary, the study has shown that channel assignment algorithms are capable of increasing the spectral diversity of multi-radio network and thus increasing the network performance in regard to metrics based on throughput. Both simple greedy strategies MICA and DGA deliver results in the same order and increase the network capacity by three times compared to the single channel network. However, both algorithms are not aware of external sources of interference. In the following chapter, we introduce our approach for external interference-aware channel assignment.

123 CHAPTER 8 External interference-aware channel assignment With the pervasive use of wireless devices in our everyday lives, channel assignment algorithms need to solve a new challenge - they need to become aware of the radio activity of co-located networks and devices. This chapter describes such an algorithm that is capable to adapt to environments with interference caused by external devices. This chapter starts with a discussion on how spectrum sensing can be used to detect radio activity of external devices. We present DES-Sense, a spectrum sensing software tool for the commodity IEEE wireless network interfaces of the DES-Testbed. Based on DES-Sense, an implementation candidate for an external interference-aware channel assignment algorithm is developed. The chapter closes with the results of the performance evaluation of the algorithm in the DES-Testbed and a comparison to DGA in multiple scenarios with dierent levels of interference. 8.1 Motivation The increasing density of WLANs in urban areas leads to signicant capacity limitations in the unlicensed 2.4 and 5 GHz bands [43]. Additionally, with the dawn of the Internet of Things, the number of wireless devices operating in the unlicensed frequency bands is dramatically increasing. It is envisioned, that by 2020, between 50 and 100 billion devices will be connected over the Internet [7]. The majority of these devices will communicate using a local Access Point or Gateway to connect to the Internet. Many of these devices will be equipped with radios operating in the unlicensed frequency spectrum. Thus, solving the challenge of spectrum scarcity is crucial for ecient wireless communications. From the point of view of the network operator, co-deployed WLAN networks as well as devices based on other technologies such as Bluetooth [45] or ZigBee [44], can be considered external, since they are not under control of the network operator. Thus, we summarize their activity with the term external interference in the remainder of this chapter. 107

124 108 Chapter 8. External interference-aware channel assignment The rising congestion of the wireless radio channels poses crucial challenges for the eld of channel assignment. To exploit the benets of spectral diversity, the channel assignment approaches have to consider not only the network-wide channel congestion 1 but also the local channel conditions which may be aected by co-located devices. Otherwise, it may happen that channels are assigned, which are already heavily congested by the radio activity of external devices. Therefore, channel assignment approaches have to be external interference-aware in exposed environments. Several challenges have to be solved for the implementation of an external interferenceaware channel assignment. The contribution of this chapter are the following A measurement-based method is developed to assess the local radio channel congestion. The DES-Chan framework has been extended to provide the data of the channel congestion to the channel assignment algorithms. An external interference-aware channel assignment (EICA) algorithm is developed using the statistics of the local channel congestion. A performance evaluation in the DES-Testbed is carried out to investigate the eciency of EICA in scenarios with dierent levels of interference. At rst, the approach to measure the congestion levels of the wireless radio channels in a distributed manner is presented. Then, EICA is introduced and the chapter nishes with the performance evaluation of EICA in the DES-Testbed. 8.2 Detecting external radio activity Challenges A crucial prerequisite for external interference-aware channel assignment is that the network nodes have to be aware of ongoing local radio activity. This can be done passively, either by detecting external devices and monitoring their radio activity or by measuring the congestion level of the available radio channels. The main challenges to detect the radio activity of external devices are the following: The location of external devices and patterns of their radio activity are usually not known before the network operation. A wide range of dierent devices based on dierent technologies operate in the unlicensed frequency spectrum. These devices can range from co-located WLAN Access Points and clients to Bluetooth and ZigBee devices, wireless keyboards, game controllers, and many more. 1 Often used synonyms for this term in the literature are channel load, channel occupancy, and channel busy fraction.

125 8.2. Detecting external radio activity 109 The trac patterns of the external devices are not known and may be dynamic over time. For instance Access Points of co-located WLAN networks may send beacons periodically, while mobile devices such as smartphones connected to these Access Points roam the network environment only in the day time. For these reasons the detection of all external wireless devices in the network environment requires much eort. One solution to measure the local radio activity is to determine the congestion levels of the available radio channels, thus knowing which channels are already heavily used. Commercial spectrum analyzers, such as WiSpy [135] and Spectrum XT [136], are hardware systems for this task that allow the detection of RF activity in the unlicensed 2.4 and 5 GHz spectrum. Alternatively, software solutions using o-the-shelf IEEE hardware can be used for the same purpose. This approach has the advantage that no additional hardware is required and it can be usually integrated into existing systems. Measuring the channel load has the benets that the channel conditions are taken into account independently of the particular devices causing the channel congestion Measuring channel congestion Measuring the channel congestion can be done in multiple ways. The standard extension IEEE k for radio resource measurements and management [137] introduces methods for interference measurements derived from the channel congestion [138]. Based on these measurements, channel load and medium sensing time histogram reports are exchanged among the network nodes. With these reports, clients connect to Access Points considering not only the signal strength but also the congestion level of the channel utilized by the Access Points. The mechanisms were introduced to distribute the network trac more evenly across the available Access Points and radio channels. Another method to measure the channel congestion is to directly access the carrier sensing statistics of the wireless network interface. Atheros-based WNICs, as used in the DES-Testbed, maintain two registers that can be used for this purpose [139, 140]. The cycle time (or time slot) counter is incremented with every clock tick and depends on the used clock for the particular Atheros chipset. The medium busy counter holds the information for how many cycles the medium has been sensed busy in regard to the clear channel assessment (CCA) function. Current drivers for the Atheros-based WNICs, such as madwifi [74] and ath5k [85] expose these statistics. Calculating the ratio of these two values results in the measured channel congestion for the captured time period. The method of measuring the channel congestion based on carrier sensing has already been used for many diverse protocols and application scenarios. These applications include interference-aware routing metrics [141], rate adaptation mechanisms for IEEE [140, 142], resource allocation [143], and localization with ranging for IEEE devices [144]. Also, as part of cognitive radio research, spectrum sensing and dynamic spectrum access (DSA) are introduced mechanisms to detect white spaces to counter the radio spectrum scarcity problem [130].

126 110 Chapter 8. External interference-aware channel assignment Value channel time channel time busy channel time rx channel time tx Description Holds the number of times lots the radio has been operating on the particular channel. Holds the number of time slots in which the medium has been sensed busy. Holds the number of time slots in which the radio was receiving a transmission. Holds the number of time slots in which the radio was transmitting a frame. Table : Overview of Atheros AR5413 carrier sensing registers DES-Sense The sensing component DES-Sense allows real-time measurements of the channel congestion in the DES-Testbed. DES-Sense calculates the channel congestion based on the statistics of the carrier sensing registers of the wireless network interface as described above. As next, we present the architecture of DES-Sense and show results of measuring the channel congestion in the environment of the DES-Testbed, which is co-located to several WLANs on the campus of the Freie Universität Berlin. Implementation DES-Sense measures the channel congestion based on the carrier sensing statistics of the wireless network interface. We retrieve the statistics by periodically reading the corresponding 32-bit registers of the wireless network interfaces with the AR5413 chipset. The ath5k driver [85] provides a wrapper function which returns the values given in Table In order to calculate the channel congestion, only the values for the channel time and channel time busy are required. With these values we can calculate the channel congestion CG c for channel c with channel time busy CG c = (8.1) channel time The result for the channel congestion is a value between 0 and 1, describing what fraction of the measurement interval the medium was sensed busy. A value CG c = 0.5 corresponds to a channel load of 50% for channel c in the measurement interval. Challenges In order to use this method for measuring the channel congestion eciently, several challenges have to be solved. First, the measurements of the channel congestion on the channels in question are performed in real-time. Depending on the radio activity patterns of the local devices, the channel congestion may be very dynamic over time, thus requiring repetitive measurements to maintain correct information. With multi-radio mesh nodes, this

127 8.2. Detecting external radio activity 111 can either be solved with a dedicated interface, that permanently runs the measurement task for the channels in question. The drawback of this approach is that the dedicated interface can not be utilized for data transmissions. Another method is to perform the monitoring measurements event-based, in case a change of the link quality is observed, for example, when the throughput on that particular link drops. With this method, a trac ow must be stopped for the duration of the measurement phase and can only be resumed after a less congested channel is found and switched to. This will lead to a higher delay for this particular ow. Another challenge results from the fact, that a wireless interface can measure the channel congestion for only one channel at a time. This means, that simultaneous measurements of all available channels are not possible with one wireless interface. With the set of available channels K and the measurement duration t c for each channel c K, the duration for a complete measurement phase is T M = c K t c (8.2) Finally, from the carrier sensing statistics alone, it can only be derived that the channel has been utilized, but not by which station. Therefore, based on the statistics we can not distinguish trac from our own network from the radio activity of external networks and devices. To solve this problem, the monitoring interface can be set in monitor mode and thus capture and analyze all received frames during the measurement interval. This way, we can identify frames sent by nodes from our network and distinguish the corresponding channel load from external radio activity. Architecture DES-Sense has been integrated into the DES-Chan framework and consists of the following two components: Sensing component - The sensing component is a daemon that periodically retrieves statistics about the channel congestion as described above. The sensing component can be congured dynamically with the set of channels C = {c 1, c 2,.., c k } that will be monitored and the duration T = {t 1, t 2,.., t k }, each channel is monitored. IPC interface - An inter process communication (IPC) interface is provided that allows algorithms to retrieve the channel congestion statistics. The algorithms can query the service via the interface to update their channel congestion statistics to fuse the information into their channel assignment decision Experimental evaluation The DES-Testbed is deployed on the premises of the computer science campus of the Freie Universität Berlin. Throughout the deployment area, Access Points of co-located WLAN networks are distributed to provide Internet access to students and research sta.

128 112 Chapter 8. External interference-aware channel assignment (a) Indoor node a6-011 at 2.4GHz (b) Indoor node a6-011 at 5GHz (c) Outdoor node t9-ext-154 at 2.4GHz (d) Outdoor node t9-ext-154 at 5GHz Figure : Exemplary results of channel congestion measurements in the environment of the DES-Testbed. A signicant channel congestion is detected on selected channels in the 2.4 GHz spectrum. The channels on the 5 GHz spectrum are not utilized. In this experimental study, we measure the basic channel congestion in the environment of the DES-Testbed while the nodes of the testbed are completely silent. We activated one wireless interface on each testbed node and measured the channel congestion for all available channels on the 2.4 and 5 GHz frequency band for 20 minutes each. Exemplary results for the measured channel congestion for one indoor and outdoor node for both frequency bands are depicted in Figure The results reveal two interesting characteristics of the environment of the DES-Testbed. First, the available channels on the 5 GHz spectrum are hardly utilized in the environment of the DES-Testbed. Second, a signicant channel congestion of up to 40% has been detected on selected channels on the 2.4 GHz band. The channels with the highest congestion are 1, 6, 11 and we can see that the adjacent channels suer from adjacent channel interference (ACI) since they overlap. Further investigation revealed that the Access Points of the infrastructure WLAN of the university is operating on exactly these channels. Most

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